# table Published articles for table. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Designing your own furniture with Claude Code can save 85% versus premium brands like Poliform DevFeed: [Designing your own furniture with Claude Code can save 85% versus premium brands like Poliform]() Original publisher: [Read original article]() Author: Pieter Levels Published: 2026-11-07T00:00:00Z Content type: opinion Language: en Sources: [@levelsio (Pieter Levels)]() Topics: [Claude Code](), [3D]() Tags: [3d](), [ai](), [bill-of-materials](), [blender](), [claude-code](), [design](), [freecad](), [furniture](), [materials](), [money](), [opus-5-5](), [sofa](), [suppliers](), [table]() ### AI overview The text describes a non-designer using Claude Code in a terminal to develop a table design from scratch, then suggests using a bill of materials to buy parts directly and build furniture. It claims this approach could cost 85% less than premium brands, illustrating the claim with a $30,000 sofa priced at $4,500 and an $8,000 table at $1,200. The excerpt does not provide a breakdown or independent verification of these estimates. It also names Blender and FreeCAD as free 3D tools that models could operate, but gives no details about their use in the table design. ### Source excerpt Claude Code can design furniture part by part and then give you a bill of materials to order each part and put it together yourself And way cheaper than big in... ## Brume: FK-safe pseudonymization for PostgreSQL test and debug copies DevFeed: [Brume: FK-safe pseudonymization for PostgreSQL test and debug copies]() Original publisher: [Read original article]() Published: 2026-11-07T00:00:00Z Content type: release Language: en Sources: [PostgreSQL news]() Topics: [PostgreSQL](), [Developer Tools](), [database-consistency]() Tags: [apache-2-0](), [data](), [database](), [debug](), [debugging](), [development](), [email](), [encryption](), [fake](), [foreign-keys](), [integrity](), [personal-data](), [postgresql](), [referential-integrity](), [synthetic](), [table](), [target](), [test](), [testing](), [tool]() ### AI overview Brume 2.1 creates deterministic, pseudonymized PostgreSQL extracts for testing, debugging, or demos while preserving foreign-key integrity. It reads from the source and transforms data before writing a selected subset to a target database or SQL file. Column strategies include synthetic values, partial masking, hashing, nullification, format-preserving encryption for identifiers, and date shifting. Stable outputs use user-provided secrets; linked_columns keeps shared values consistent even without formal foreign keys. Row filters and configurable traversal of parent and child foreign keys produce smaller coherent extracts. A reviewable brume.yml configures table and column strategies, semantic types, and JSONB paths. The text says Brume supports PostgreSQL 12-18 and describes pseudonymization as one GDPR measure; it does not claim pseudonymization alone ensures compliance. ### Source excerpt Brume 2.1, an open source (Apache 2.0) tool that creates deterministic, foreign-key-safe pseudonymized copies of PostgreSQL databases for testing, debugging and demos, is now available. Why Brume? Development teams often need realistic data to reproduce bugs, run tests or prepare demos, but copying production exposes real personal data. Classic masking scripts also tend to break referential integrity, which forces you to disable triggers, suspend constraints or repair broken joins by hand. Brume copies a targeted subset of a source database to a target database or to a .sql file, and transforms personal data on the fly, before anything is written. Foreign keys stay valid end-to-end on the target. Brume only reads from the source database. Key features Column-level strategies: FAKE for realistic synthetic values, MASK for partial reveals, HASH for one-way hashing, NULLIFY to drop sensitive data, FPE_ID / FPE_UUID for format-preserving encryption of identifiers, and DATE_SHIFT to move dates while keeping their distribution. Deterministic output: transformations are keyed by your own secrets. The same input with the same secret always gives the same output, across all releases of a major version, so test fixtures and debug sessions stay stable from one run to the next. Referential integrity: format-preserving encryption rewrites primary keys and the foreign keys pointing to them consistently. With linked_columns, a value shared between tables that have no formal foreign key (for example, an email in both users and audit_logs) gets the same pseudonym everywhere. Subsetting: define row-level filters on any table (created_at >= '2025-01-01', tenant_id = 42...). Brume follows foreign keys up to a configurable depth, towards parents and children, to produce a small, self-contained and coherent extract. Declarative configuration: a single brume.yml file describes the strategy for each table and column, with semantic types (EMAIL, PHONE, IBAN, ADDRESS...) and JSONB paths to t ## Kafka to ClickHouse: Streaming Ingestion, Duplicates & Tombstones Guide DevFeed: [Kafka to ClickHouse: Streaming Ingestion, Duplicates & Tombstones Guide]() Original publisher: [Read original article]() Author: Seán Whelan Published: 2026-11-06T12:26:41Z Content type: article Language: en Sources: [Estuary]() Topics: [Apache Kafka](), [Event-Streaming](), [self-hosted databases vs managed services](), [table-partitioning](), [schema-evolution](), [Amazon MSK](), [Confluent Cloud](), [Apache Iceberg]() Tags: [clickhouse](), [compare](), [connect](), [delivery](), [engine](), [guarantees](), [guide](), [kafka](), [stream](), [streaming](), [table]() ### AI overview Kafka ingestion into ClickHouse is generally at least once, so replayed messages can create duplicates; reliable designs need batching and tables suited to event or keyed data. The article compares the Kafka table engine, ClickPipes, the Kafka Connect sink, and managed pipelines: the engine runs consumers on ClickHouse servers, ClickPipes is recommended for ClickHouse Cloud, and the sink fits teams already running Kafka Connect. For event streams, store partition and offset in a ReplacingMergeTree sorting key; keyed topics with tombstones need ReplacingMergeTree keyed by message key and queries with FINAL. Tombstone support differs: ClickPipes can delete rows in exactly-once mode or mark them deleted, while the Connect sink lists deletes as unsupported and the table engine's documentation does not describe null-value messages. Retention limits recovery, and compaction can remove tombstones after the configured retention period. Large, infrequent inserts reduce part pressure; monitor lag and active parts. The article also describes Estuary capture and materialization, which keeps Kafka progress in its own checkpoints and writes to ClickHouse on a configurable schedule. ### Source excerpt Stream Kafka to ClickHouse with the Kafka table engine, ClickPipes or Kafka Connect. Compare 4 methods, delivery guarantees and duplicate handling. ## CodeSOD: Only Create Me if I Exist DevFeed: [CodeSOD: Only Create Me if I Exist]() Original publisher: [Read original article]() Author: Remy Porter Published: 2026-11-05T06:30:00Z Content type: article Language: en Sources: [The Daily WTF]() Topics: [sql-server](), [sql-database](), [SQL]() Tags: [codesod](), [constraint](), [database](), [foreign-keys](), [sql](), [sql-server](), [table]() ### AI overview The excerpt shows a SQL Server constraint that makes `ListingDetails.Id` a foreign key to the same table's `Id`. Unlike a typical self-reference between different rows, each record satisfies this constraint with its own ID, so the author says it appears to work as a no-op. The author notes they have not tested it personally and bases that conclusion on cursory searching. ### Source excerpt Foreign keys are an important part of making sure the entities in our relational database can be connected back together and queried correctly. It's part of proper data normalization. This isn't shocking, of course: the order line table has a foreign key over to the products table, so that every line item points to a product we sell. That's database 101. Database 201, you'd also think about the times where you have a hierarchy of records, where say, every Employee has a ManagerId, which is a foreign key back to the Employee table itself (and in database 301, you'll understand why having a ManagerId field is a terrible choice, but that's neither here nor there). The important thing here is that foreign keys are very common, and foreign keys pointing back to the same table are also very common. But it can still go very wrong. Roman sends us this foreign key definition in a database they maintain. ALTER TABLE [dbo].[ListingDetails] WITH CHECK ADD CONSTRAINT [FK_Listings_Listings] FOREIGN KEY([Id]) REFERENCES [dbo].[ListingDetails] ([Id]) This ensures that the ListingDetails table's Id field is a foreign key over to the ListingDetails table's Id field. A field is a foreign key back to itself. Roman writes: "I can't believe SQL Server even allows this..." I can. While I haven't personally tried this out, a bit of cursory searching implies that this does actually work- the record satisfies its own constraint, so you can still add it to the database. It's basically a no-op, which isn't great, but it's not SQL Server's job to ensure you keep all your toes because they've removed all the footguns. 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Download today! ## AmbiHTMLBench: A Benchmark for HTML-based Visually Rich Document parsing DevFeed: [AmbiHTMLBench: A Benchmark for HTML-based Visually Rich Document parsing]() Original publisher: [Read original article]() Author: Boyu Niu, Yingjie Xiong, Shurui Li, Linhan Pang, Yuntong Zhang, Han Wang, Hao Wu, Kai Zuo, Xu Tang, Xuanhe Zhou, Fan Wu Published: 2026-11-05T05:00:00Z Content type: article Language: en Sources: [arXiv: Artificial Intelligence]() Topics: [HTML](), [Benchmark](), [Fine-tuning](), [Markdown]() Tags: [annotations](), [appearance](), [benchmark](), [computer-science](), [cs-ai](), [error](), [html](), [layout](), [markdown](), [model](), [multimodal](), [organization](), [pages](), [parsing](), [rendering](), [spatial](), [structure](), [table](), [testbed](), [text](), [visual-hierarchy]() ### AI overview AmbiHTMLBench evaluates whether models can reconstruct real-world documents as executable HTML that preserves content, organization, and appearance. It includes 1,115 annotated pages and compares systems using rendered-block alignment for table fidelity, page-level text error, and two-dimensional layout consistency, alongside a reference-anchored assessment that aligns LLM judges with human preferences. Among ten primary multimodal systems, the top objective score was 0.8092, but a different system won the subjective evaluation, suggesting the protocols capture complementary aspects of fidelity. The authors also report that task-specific fine-tuning of a small-parameter expert model performs well on document-to-HTML reconstruction. ### Source excerpt arXiv:2611.06449v1 Announce Type: new Abstract: Traditional document parsing systems use Markdown as their output representation, which is effective for linear text but does not fully preserve fine-grained spatial structure, table geometry, visual hierarchy, or rendering semantics. We show that HTML is advantageous for heterogeneous and layout-sensitive documents whose representations must remain editable, inspectable, and reusable, and introduce AmbiHTMLBench to systematically study this setting. In this benchmark, given a real-world document, a model must recover executable HTML that preserves its content, organization, and visual appearance. The benchmark contains 1,115 diverse pages with structured annotations and evaluates predictions in two different ways: The objective protocol aligns ground-truth and predicted rendered blocks and measures table fidelity, page-level text error, and a two-dimensional layout consistency; a complementary reference-anchored protocol measures perceived fidelity by aligning LLM judges with human preferences. Across ten primary multimodal systems, the best objective score is 0.8092, while the subjective winner is different, demonstrating that automatic decomposition and human-perceived fidelity capture complementary properties. We further fine-tune a small-parameter expert model and find that task-specific adaptation yields good performance on document-to-HTML reconstruction task. These results position AmbiHTMLBench as a diagnostic testbed for building smarter and more logical document parsing systems. ## Stream your customers' audit logs into their own Snowflake warehouse DevFeed: [Stream your customers' audit logs into their own Snowflake warehouse]() Original publisher: [Read original article]() Author: WorkOS Published: 2026-11-05T00:00:00Z Content type: tutorial Language: en Sources: [WorkOS]() Topics: [Snowflake streaming data architecture](), [audit](), [stream-processing](), [telemetry]() Tags: [2](), [2026](), [3](), [audit](), [audit-logs](), [auth](), [customer](), [customers](), [event](), [fixed](), [key](), [logs](), [pair](), [schema](), [snowflake](), [stream](), [table](), [up](), [warehouse](), [with]() ### AI overview WorkOS Log Streams can write each organization's audit events as rows in the customer's own Snowflake account, where the customer controls storage and retention. Setup requires a dedicated user authenticated by an RSA key pair, a table with the documented schema, and a default role granted access to the warehouse, database, schema, and table; accounts with network policies must allow WorkOS's published IP addresses. The dashboard and Admin Portal configure the same stream, but the portal lets the customer provide credentials directly so the private key need not pass through support. Monitor actual delivery with a customer-specific freshness query in Snowflake: an Active state alone does not establish that rows are arriving, and the article says not to assume stop-delivery notifications. Error or Invalid streams require fixing the cause and revalidating. Delivery resumes from the last successful event, subject to the organization's WorkOS retention window; previously delivered rows remain subject to the customer's own warehouse policy. ### Source excerpt Each audit event lands as a row in your customer's own Snowflake table, with key-pair auth, a fixed schema, grants up front, and a freshness check. ## Before You Model a Single Table: Data Modelling Questions to Ask for AI Agents in Snowflake DevFeed: [Before You Model a Single Table: Data Modelling Questions to Ask for AI Agents in Snowflake]() Original publisher: [Read original article]() Author: Cesar Segura Published: 2026-11-03T19:31:02Z Content type: article Language: en Sources: [Snowflake Builders]() Topics: [schema design](), [semantic-layer](), [JOIN](), [text2sql](), [Data Quality](), [knowledge-engineering](), [business logic](), [AI Architecture]() Tags: [agent](), [agents](), [aggregation](), [ai](), [ai-agents](), [analyst](), [architect](), [article](), [business](), [business-analyst](), [column](), [cortex](), [cortex-agent](), [data](), [data-modelling](), [generative-ai-tools](), [governance](), [model](), [modelling](), [ontology](), [questions](), [snowflake](), [table]() ### AI overview AI agents infer query logic from schemas, so ambiguous grain, joins, dates and metric meanings can produce incorrect SQL. The article recommends documenting and testing each fact table's grain, exposing clear business columns, defining relationships and metric logic explicitly, and using scoped Snowflake Semantic Views over curated data. It distinguishes the executable query and metric layer (Semantic Views) from governed business definitions (Business Ontology) and AI-inferred context (Cortex Sense); ontology formulas document meaning, while Cortex Analyst executes the Semantic View expression, so they must stay aligned. It also advises profiling performance under realistic workloads and repeatedly evaluating representative business questions, including join, double-counting, ambiguity and permission cases. Business Ontology and Cortex Sense are noted as preview features as of October 2026. ### Source excerpt A Snowflake Architect's guide to designing data that AI agents can actually understand Questions prior to modelling in Snowflake for your agents Every few weeks someone asks me the same thing: "We want a Cortex Agent on top of our data. How should we model it?" The honest answer is that most of the questions behind that sentence are not about the agent. They are modelling questions we should have answered years ago, and an agent now exposes them in public. A business analyst carries a lot of tacit knowledge: which date really means "sale", which orders are cancelled, what "revenue" means for Finance versus Sales. An agent has none of that. It reads your schema, infers, and writes SQL. Every ambiguity becomes an inference, and every inference is a chance for a wrong join, filter or aggregation. In this article we will walk through the six questions I ask teams before they start modelling: grain, shape, semantic views, business meaning, performance and evaluation. We will close with a checklist you can take to your next design session. Index 1. Why agents change the rules 2. Question 1: What is the grain, and does every column mean one thing? 3. Question 2: Star, snowflake or wide table, and how many joins are too many? 4. Question 3: How big should a Semantic View be? 5. Question 4: Who owns the meaning of a metric? 6. Question 5: Does physical performance still matter? 7. Question 6: How will we know it works? The Big Picture Conclusions References About Me 1. Why agents change the rules The first thing I explain is that an agent solution is not one model; it is a stack of layers, each with a different job. Confusing them is the root of most disappointing proofs of concept. Curated physical model: tables, views, facts, dimensions, types, keys and quality tests. It represents the data faithfully and consistently. Semantic View: the executable semantic layer. It describes how to query a domain and how to calculate its metrics: logical tables, relationships, dimensions ## Optimizing Columnar Storage in Tanzu Greenplum, Part 2: Accelerating Hash Joins with Early Join Filtering DevFeed: [Optimizing Columnar Storage in Tanzu Greenplum, Part 2: Accelerating Hash Joins with Early Join Filtering]() Original publisher: [Read original article]() Author: VMware Community Published: 2026-11-03T05:29:30Z Content type: article Language: en Sources: [VMware]() Topics: [JOIN](), [performance-engineering]() Tags: [benchmark](), [bloom](), [bloom-filter](), [columnar](), [cpu](), [greenplum](), [hash](), [join](), [modern-apps](), [optimizing](), [payload](), [query](), [storage](), [table](), [vmware](), [vmware-tanzu-greenplum]() ### AI overview Tanzu Greenplum 7.9's Early Join Filtering (EJF) can reduce work on selective hash joins by using only join keys and a Bloom filter built from the join's build side to discard non-matching rows during scanning, before payload columns are read. The optimizer chooses EJF only when it estimates the path is cheaper than a standard hash join. Safeguards disable filtering when the Bloom filter is ineffective during build or probe. In the reported TPC-DS Scale 1000 results, EJF reduced total workload time by 14.83%; 91 of 99 queries improved, two were unchanged, and six regressed, with a maximum slowdown of 12.09%. These are benchmark results reported in the supplied excerpt. ### Source excerpt This post walks through Early Join Filtering (EJF) in Tanzu Greenplum 7.9. This extends the "read less, decide early" architecture to hash joins as EJF filters out non-matching rows before payload columns are ever materialized, delivering the query speed of an indexed table without the maintenance overhead. The post Optimizing Columnar Storage in Tanzu Greenplum, ... Continued The post Optimizing Columnar Storage in Tanzu Greenplum, Part 2: Accelerating Hash Joins with Early Join Filtering appeared first on VMware Blogs. ## Fidelity Collapses with Table Size, Not Turn Count: Delivery Architecture in a Deployed Tool-Using Agent DevFeed: [Fidelity Collapses with Table Size, Not Turn Count: Delivery Architecture in a Deployed Tool-Using Agent]() Original publisher: [Read original article]() Author: Tung Thanh Hoang Published: 2026-11-03T05:00:00Z Content type: news Language: en Sources: [arXiv: Artificial Intelligence]() Topics: [AI Context Management](), [Agent Harness]() Tags: [agent](), [architecture](), [arguments](), [channel](), [cs-ai](), [cs-cl](), [delivered](), [delivery](), [filter](), [full](), [model](), [models](), [production](), [records](), [sampling](), [side-channel](), [size](), [structured](), [synthesis](), [table](), [tool](), [travel]() ### AI overview In a deployed award-travel agent, keeping tool records in session state and delivering filtered rows through a typed side channel substantially improved correctness over placing full tables in the model's context. Across 24 dialogues and 339 scored turns per architecture, BYPASS was fully correct on 92.3% of turns versus 45.7% for SYNTHESIS. SYNTHESIS accuracy fell as tables grew: it was near-perfect at 10 rows or fewer and 8% or less beyond about 74 rows. In a content-controlled test, the collapse threshold varied by model, from under 10 rows for GPT-4o-mini to about 198 or more for Gemini 2.5 Pro; BYPASS remained size-flat across the four tested models. Beyond 100 rows, SYNTHESIS's rare correct answers cost roughly 200 times as much as BYPASS's reported $0.012 per correct answer. These results concern the tested agent and setups; the authors frame typed side-channel delivery as a correctness choice as well as a rendering choice. ### Source excerpt arXiv:2611.00442v1 Announce Type: cross Abstract: When a multi-turn agent delivers a tool's structured records into the model's context and re-emits them each turn, every turn can corrupt data the tool computed correctly. We measure this in a production award-travel agent: two architectures over identical frozen payloads, lineage-matched prompts and sampling, differing only in delivery architecture. SYNTHESIS feeds full record sets into the planner's context and answers follow-ups in-head; BYPASS, the deployed system, keeps records in session state, translates follow-ups into arguments for a deterministic filter, and delivers rows over a typed side channel. Across 24 dialogues (339 scored turns per condition), BYPASS scores 92.3% of turns fully correct, SYNTHESIS 45.7% (paired gap +46.6pp [+35.7, +57.1]). Varying delivered-table size by capture date, SYNTHESIS collapses from near-perfect at 10 rows or fewer to 8% or less beyond about 74 rows. On a content-controlled prefix ladder the collapse threshold orders by model capability, from under 10 rows (GPT-4o-mini) to about 198 or more (Gemini 2.5 Pro), while BYPASS stays size-flat for all four models. The collapse is expensive: beyond 100 rows, where almost no SYNTHESIS answer is correct, each correct one costs roughly 200x BYPASS's flat \$0.012. Practitioners converged on typed side-channel delivery for rendering, not correctness; we show it was, unknowingly, also a correctness decision. ## Why Tabular Foundation Models Excel on Relational Data They Have Never Seen DevFeed: [Why Tabular Foundation Models Excel on Relational Data They Have Never Seen]() Original publisher: [Read original article]() Author: Linjie Xu, Yiqun Hu, Jixiang He, David Wipf Published: 2026-11-03T05:00:00Z Content type: article Language: en Sources: [arXiv: Artificial Intelligence](), [arXiv: Databases](), [arXiv: Machine Learning]() Topics: [Predictive AI](), [AI Foundation Models](), [Machine learning](), [rdbms](), [Structured-data](), [Embeddings](), [AI Integration Strategies]() Tags: [agentic-systems](), [column](), [context](), [cs-ai](), [cs-db](), [cs-lg](), [data](), [database](), [distributions](), [effects](), [embeddings](), [experiments](), [fixed](), [foundation-models](), [future](), [interpretability](), [learning](), [models](), [prediction](), [properties](), [table](), [target](), [zero]() ### AI overview The paper reports that single-table tabular foundation models can predict target values in relational databases despite having no relational-data pretraining, when paired with parameter-free relational encoders. It confirms this performance across a broader set of experiments and proposes complementary explanations: the encoders turn relational effects into fixed-length embeddings that loosely resemble the distributions the models were designed for, allowing some single-table properties, including convergent posterior predictive distributions, to carry over. The authors suggest this analysis may help guide interpretable relational foundation models and their integration into agentic systems. ### Source excerpt arXiv:2611.02936v1 Announce Type: cross Abstract: Given a relational database (RDB) storing heterogeneous tabular information, how can we predict missing (or future) values in some target column of interest? As candidate targets are numerous in enterprise settings, it is preferable to avoid learning a new model from scratch each time there is a new prediction task. Frozen foundation models (FMs) based on in-context learning (ICL) and RDB-specific pre-training provide a viable solution, but the ideal design still remains largely unresolved. Perhaps unexpectedly, preliminary evidence has recently shown that existing single-table FMs (aka TFMs), when combined with suitable parameter-free relational encoders, can be quite competitive in practice despite zero exposure to RDB pre-training or relational data of any kind. In this work we first empirically confirm this phenomenon with a more diverse array of experiments. We then turn to our main contribution, namely, surfacing multiple complementary explanations for why TFMs behave so effectively. These explanations ultimately hinge on innate synergies whereby parameter-free encoders map relational effects to fixed-length embeddings that loosely mimic distributions for which TFMs were originally designed; in so doing, desirable single-table properties transfer to relational settings (e.g., convergent posterior predictive distributions). Collectively, our analysis forges new pathways for improving RDB FMs, particularly when we prioritize interpretability and inevitable integration within agentic systems. ## Create your first Appwrite database and add your first row DevFeed: [Create your first Appwrite database and add your first row]() Original publisher: [Read original article]() Author: Atharva Deosthale Published: 2026-11-03T00:00:00Z Content type: tutorial Language: en Sources: [Appwrite Blog]() Topics: [Appwrite](), [Database](), [CRUD](), [rdbms]() Tags: [app](), [apps](), [auth](), [database](), [npm](), [permissions](), [table](), [tutorials](), [write]() ### AI overview The guide creates an Appwrite TablesDB database on the Free plan, adds a `tasks` table with typed columns and a `done` index, and demonstrates inserting and listing rows through the Appwrite SDK. Granting the All users role Create permission and enabling row-level security lets signed-in users create tasks while limiting each user to their own rows. The example filters for open tasks and orders newest first. Console-created rows are useful for testing. TablesDB uses Serverless compute on every plan with no compute fee; DocumentsDB, VectorsDB, PostgreSQL, and MySQL use dedicated compute on Pro and Enterprise, starting at $10 per database monthly. The text also describes changing permissions to Any with Read access, which allows all visitors to read every row. ### Source excerpt Create a TablesDB database in Appwrite, add a table with typed columns, choose who can read and write, then add your first row and use it from your app. ## Certifying Retraining Regret through Residual Structure DevFeed: [Certifying Retraining Regret through Residual Structure]() Original publisher: [Read original article]() Author: Peida Li Published: 2026-11-02T05:00:00Z Content type: article Language: en Sources: [arXiv: Machine Learning]() Topics: [Machine learning]() Tags: [arxiv](), [certification](), [continuous](), [cs-lg](), [dimension](), [energy](), [error](), [examples](), [fallback](), [features](), [global](), [independent](), [machine-learning](), [masks](), [model](), [rank](), [real-world](), [scale](), [table]() ### AI overview The paper presents verifiable residual conditions for bounding a fixed ridge predictor's regret against full-feature retraining across every continuous completion of an interval-valued table. The bound is independent of nuisance-feature scale and rank; observed-row anchors make the conditions computable, while exact row geometry and global search provide upper bounds and feasible original-table witnesses. A moment fallback and finite-scale contraction support implementation. In four real-world tasks, the method certified twenty rank-three instances to width 10⁻⁵, with primary median widths better than centered moments. Joint residual refinement certified twenty additional response-independent masks to that precision within 76.21 seconds. For a fixed-point predictor contract, pure energy certified 13 of 15 stopping decisions in ten seconds, compared with 5 for moments; refinement certified all 15 within 91.68 seconds at the same objective threshold. ### Source excerpt arXiv:2610.41410v1 Announce Type: new Abstract: We certify a fixed ridge predictor's regret against full-feature retraining over every continuous completion of an interval-valued table. Our main theorem gives verifiable original-row residual conditions for a uniform error bound independent of nuisance-feature scale and rank, with an asymptotically sharp leading constant and quantitative leakage stability. Full-rank constructions separate certification dimension from feature dimension, including examples requiring directions beyond the candidate. Observed-row anchors make the conditions computable, while exact row geometry and global search return valid upper bounds and feasible original-table witnesses. A finite-scale contraction and common-domain moment fallback implement the structural result. Four real-world tasks retain all model features and certify twenty rank-three instances to width $10^{-5}$. Their primary median widths improve over centered moments. Joint residual refinement additionally certifies all twenty new response-independent masks to the same precision within 76.21 seconds. For a concrete fixed-point predictor contract, pure energy certifies thirteen of fifteen stopping decisions in ten seconds, versus five for moments. Refinement certifies all fifteen within 91.68 seconds at the same objective threshold. ## TILE: Bridging LLMs and Table Foundation Models for Tabular Learning with Strings DevFeed: [TILE: Bridging LLMs and Table Foundation Models for Tabular Learning with Strings]() Original publisher: [Read original article]() Author: F\\'elix Lefebvre, David Holzm\\"uller, Gioia Blayer, Myung Jun Kim, Marine Le Morvan, Ga\\"el Varoquaux Published: 2026-11-02T05:00:00Z Content type: article Language: en Sources: [arXiv: Machine Learning]() Topics: [Machine learning](), [Embeddings](), [AI Foundation Models](), [Large Language Model]() Tags: [bridging](), [columnar](), [context](), [cs-lg](), [datasets](), [embeddings](), [features](), [foundation-models](), [layer](), [learning](), [llms](), [next-token-prediction](), [numbers](), [prediction](), [real-world](), [serialization](), [strings](), [table]() ### AI overview TILE combines LLM embeddings with table foundation models for tables containing free-form strings and numeric or categorical columns. It serializes each row to give the LLM cross-column context, extracts intermediate-layer representations, pools by column to avoid mean pooling's length bias, and bypasses the LLM for numbers and categories. The abstract reports that cell-wise LLM features do not improve with larger models, while TILE's features improve steadily with model size. Across 100 datasets, TILE beats the strongest baseline on 90% of splits across seven LLMs and ten tabular learners, and ranks first on STRABLE's 108 datasets. ### Source excerpt arXiv:2610.17065v2 Announce Type: replace Abstract: Real-world tables are often heterogeneous, combining numerical features with free-form string columns. Table foundation models (TFMs) excel on numerical tables but require strings to be encoded numerically, while large language models (LLMs) hold semantic knowledge but are not designed for tabular predictions. Most existing bridges use LLMs as plug-in encoders for cell strings, yet how to extract representations effectively for tabular learning remains underexplored. We show that the interface between LLMs and tables matters as much as the LLM itself. Applied cell-wise, larger LLMs do not give better features. With our interface, the same LLMs improve steadily with size. TILE (Table-Interfaced LLM Embeddings) combines four ingredients: row serialization to provide cross-column context, intermediate-layer extraction to avoid layers specialized for next-token prediction, columnar pooling to remove mean pooling's length bias, and by-pass features to route numbers and categories past the LLM. Paired with TFMs, TILE sets a new state of the art for tables with strings. Across 100 datasets, it beats the strongest baseline on $90\%$ of splits, with gains across 7 LLMs and 10 tabular learners, and ranks first on STRABLE's 108 datasets. ## Towards Universal Tabular Embeddings: A Benchmark Across Data Tasks DevFeed: [Towards Universal Tabular Embeddings: A Benchmark Across Data Tasks]() Original publisher: [Read original article]() Author: Liane Vogel, Kavitha Srinivas, Niharika D'Souza, Sola Shirai, Oktie Hassanzadeh, Horst Samulowitz Published: 2026-11-02T05:00:00Z Content type: article Language: en Sources: [arXiv: Databases](), [arXiv: Machine Learning]() Topics: [Embeddings](), [Representation learning](), [AI Foundation Models](), [Machine learning](), [Benchmark]() Tags: [benchmark](), [column](), [comparison](), [cs-db](), [cs-lg](), [domains](), [embeddings](), [foundation](), [foundation-models](), [guidance](), [practical](), [prediction](), [real-world](), [retrieval](), [search](), [semantic](), [semantic-search](), [table](), [tabular-data](), [tasks](), [test]() ### AI overview TEmBed (Tabular Embedding Test Bed) provides a unified benchmark for comparing tabular representation learning models across cell, row, column, and table levels. It addresses the difficulty of comparing methods evaluated in separate task-specific settings. The reported results find that the best model depends on both the task and representation level, so there is no single approach identified as best across them. The abstract says the benchmark offers guidance for choosing embeddings in applications such as table retrieval, semantic search, and table-based prediction, and supports work toward more general-purpose tabular representations. The supplied text contains only the abstract, not detailed results or model-by-model comparisons. ### Source excerpt arXiv:2604.21696v3 Announce Type: replace-cross Abstract: Tabular foundation models aim to learn universal representations of tabular data that transfer across tasks and domains, enabling applications such as table retrieval, semantic search and table-based prediction. Despite the growing number of such models, it remains unclear which approach works best in practice, as existing methods are often evaluated under task-specific settings that make direct comparison difficult. To address this, we introduce TEmBed, the Tabular Embedding Test Bed, a unified benchmark for systematically evaluating tabular embeddings across four representation levels: cell, row, column, and table. Evaluating a diverse set of tabular representation learning models, we show that which model to use depends on the task and representation level. Our results offer practical guidance for selecting tabular embeddings in real-world applications and lay the groundwork for developing more general-purpose tabular representation models. ## Postgres 19 is late, and it's AI's fault DevFeed: [Postgres 19 is late, and it's AI's fault]() Original publisher: [Read original article]() Author: Robert Haas (noreply@blogger.com) Published: 2026-10-30T15:53:50Z Content type: opinion Language: en Sources: [Robert Haas]() Topics: [releases](), [codex](), [CI/CD]() Tags: [2026](), [ai](), [beta](), [bugs](), [delay](), [delete](), [feature](), [features](), [graph](), [pgq](), [postgres](), [postgres-19](), [postgresql](), [postgresql-19](), [release](), [releases](), [split](), [sql](), [table](), [update]() ### AI overview The author attributes PostgreSQL 19's likely delay until November and several late feature reverts to a surge in bugs reported by people using LLMs. From April 8 to September 25, commits to the release branch rose from 724 in 2025 to 1,169 in 2026; commits to the preceding stable branch rose from 248 to 433. LLMs can help find bugs and draft fixes, but fixes still require human review, and reviewers' time has shifted toward bugs and security issues in supported branches. That left less time to review PostgreSQL 19 and decide what to revert. The author considers the short delay a consequence of prioritizing quality, while warning that the growing backlog of newly discoverable bugs may challenge future maintenance and innovation. ### Source excerpt Over the past decade, the timing of PostgreSQL major releases has become very predictable: no earlier than the middle of September, and no later than the middle of October. This year is going to be different. PostgreSQL 19 most likely won't be released until November, and it's not impossible that the project could decide to postpone the release further. Moreover, this year, several rather large features were reverted quite late in the beta period: UPDATE/DELETE FOR PORTION OF, SQL Property Graph Queries (SQL/PGQ), and ALTER TABLE ... MERGE PARTITION/SPLIT PARTITION were all reverted between August 27 and September 15. While major feature reverts are not especially uncommon immediately after feature freeze, which each year occurs on April 8, it is unusual to see multiple major features get pulled from the release at such a late date. So, what happened? Read more " ## Anagent For Enhancing Scientific Table & Figure Analysis DevFeed: [Anagent For Enhancing Scientific Table & Figure Analysis]() Original publisher: [Read original article]() Author: Xuehang Guo, Zhiyong Lu, Tom Hope, Qingyun Wang Published: 2026-10-30T04:00:00Z Content type: article Language: en Sources: [arXiv: Artificial Intelligence]() Topics: [Multi Agent Systems](), [AI for science](), [Artificial Intelligence](), [llm-reasoning](), [Fine-tuning](), [Complex AI tasks]() Tags: [agent](), [agent-framework](), [agents](), [ai](), [analysis](), [artificial-intelligence](), [benchmark](), [challenges](), [complexity](), [context](), [cs-ai](), [cs-cl](), [domain](), [drawing](), [evaluation](), [execution](), [figure](), [framework](), [generate](), [improvements](), [multi-agent](), [table]() ### AI overview Anagent targets scientific table and figure analysis, a task complicated by varied structures, multimodal evidence, and long contexts. The authors introduce AnaBench, with 63,178 instances across nine scientific domains and seven complexity dimensions, and a four-agent framework: Planner breaks tasks into subtasks, Expert retrieves information using targeted tools, Solver synthesizes findings, and Critic iteratively assesses and refines the result across five dimensions. Modular supervised fine-tuning and specialized reinforcement learning optimize agent capabilities while supporting collaboration. Across 170 subdomains, the authors report improvements of up to 13.43% in training-free settings and 42.12% with fine-tuning. The supplied evidence is an abstract, so it does not specify evaluation baselines or the detailed assessment criteria. ### Source excerpt arXiv:2602.10081v3 Announce Type: replace-cross Abstract: In scientific research, analysis requires accurately interpreting complex multimodal knowledge, integrating evidence from different sources, and drawing inferences grounded in domain-specific knowledge. However, current artificial intelligence (AI) systems struggle to consistently demonstrate such capabilities. The complexity and variability of scientific tables and figures, combined with heterogeneous structures and long-context requirements, pose fundamental obstacles to scientific table \& figure analysis. To quantify these challenges, we introduce AnaBench, a large-scale benchmark featuring $63,178$ instances from nine scientific domains, systematically categorized along seven complexity dimensions. To tackle these challenges, we propose Anagent, a multi-agent framework for enhanced scientific table \& figure analysis through four specialized agents: Planner decomposes tasks into actionable subtasks, Expert retrieves task-specific information through targeted tool execution, Solver synthesizes information to generate coherent analysis, and Critic performs iterative refinement through five-dimensional quality assessment. We further develop modular training strategies that leverage supervised finetuning and specialized reinforcement learning to optimize individual capabilities while maintaining effective collaboration. Comprehensive evaluation across 170 subdomains demonstrates that Anagent achieves substantial improvements, up to $\uparrow 13.43\%$ in training-free settings and $\uparrow 42.12\%$ with finetuning, while revealing that task-oriented reasoning and context-aware problem-solving are essential for high-quality scientific table \& figure analysis. Our project page: https://xhguo7.github.io/Anagent/. ## Octopus: Agentic Multi-Table Discovery over Tabular Data Lakes DevFeed: [Octopus: Agentic Multi-Table Discovery over Tabular Data Lakes]() Original publisher: [Read original article]() Author: Wen-Zhi Li, Sainyam Galhotra Published: 2026-10-30T04:00:00Z Content type: article Language: en Sources: [arXiv: Databases]() Topics: [data-engineering](), [Structured-data](), [Agentic AI](), [Large Language Model](), [AI-driven information retrieval](), [schema-evolution](), [hybrid-search]() Tags: [adaptive](), [agent](), [agentic](), [analytics](), [api](), [benchmark](), [claude](), [claude-code](), [code](), [column](), [cs-db](), [data-discovery](), [data-lakes](), [datasets](), [discovery](), [execution](), [experimental](), [framework](), [octopus](), [table](), [tabular-data]() ### AI overview Octopus is a lightweight agentic framework for finding relevant tables when a natural-language question may require information from multiple tables. It combines lightweight offline semantic guidance with query-time access to raw tabular data; an LLM agent orchestrates semantic column search, structured tabular operations, and general-purpose execution, using intermediate observations to locate and verify tables. On a new benchmark spanning four independent-discovery and two join-based datasets, Octopus achieved the highest macro-F1 on all six. The abstract reports lower offline preprocessing overhead than Pneuma and lower online latency and API cost than Claude Code. With the same NL2SQL agent, it also produced substantially higher downstream question-answering F1 than Claude Code. No scores or evaluation details are provided in the supplied excerpt. ### Source excerpt arXiv:2601.02304v2 Announce Type: replace Abstract: Tabular data discovery aims to identify the tables relevant to a natural-language question from a large collection of heterogeneous data. Existing systems typically assume that a question is answered by a single table and rely on retrieval pipelines constructed through substantial offline preprocessing. These assumptions become restrictive when relevant information is distributed across multiple tables. In this paper, we present Octopus, a lightweight agentic framework for multi-table data discovery. Octopus follows a thin-harness design that combines lightweight offline semantic guidance with adaptive access to raw tabular data. At query time, an LLM agent dynamically orchestrates semantic column search, structured tabular operations, and general-purpose execution to locate and verify relevant tables based on intermediate observations. We further introduce a benchmark that explicitly evaluates multi-table data discovery, covering both settings where multiple tables independently answer a question and settings where multiple tables must be joined. Experimental results across four independent-discovery datasets and two join-based datasets show that Octopus achieves the highest macro-F1 on all six datasets. It also substantially reduces offline preprocessing overhead relative to Pneuma and online latency and API cost relative to Claude Code. When coupled with the same NL2SQL agent, Octopus yields substantially higher downstream question-answering F1 than Claude Code, showing that improved table discovery provides more complete evidence for downstream analytics. Code is available at https://github.com/wenzhilics/octopus. ## DeFT-RAG: Incrementally Decomposing Multi-Hop Questions into Self-Contained Sub-Questions with Reusable Answers DevFeed: [DeFT-RAG: Incrementally Decomposing Multi-Hop Questions into Self-Contained Sub-Questions with Reusable Answers]() Original publisher: [Read original article]() Author: Dingyi Kang, Bingzhe Li Published: 2026-10-30T04:00:00Z Content type: article Language: en Sources: [arXiv: Artificial Intelligence]() Topics: [Retrieval Augmented Generation (RAG)](), [Large Language Model](), [llm-reasoning]() Tags: [cache](), [cs-ai](), [cs-cl](), [cs-ir](), [decision-model](), [dependencies](), [framework](), [graph](), [language](), [language-model](), [large-language-model](), [lightweight](), [llm](), [pairs](), [planning](), [qa](), [question](), [qwen3-6](), [rag](), [retrieval](), [retrieval-augmented-generation](), [retrieval-augmented-generation-rag](), [table]() ### AI overview DeFT-RAG handles multi-hop retrieval by generating one self-contained sub-question at a time from the original question and answered question-answer pairs. It uses rule-based updates to track known answers, a fine-tuned lightweight model for yes/no decisions, and a cache that reuses answers to semantically equivalent sub-questions. On MuSiQue, 2WikiMultiHopQA, and HotpotQA, it reportedly achieved the highest accuracy among 13 baselines with both gpt-4o-mini and Qwen3.6, while using fewer LLM input tokens than other iterative methods. In a cache shared across 1,000 MuSiQue questions, 23.1% of sub-questions were answered from cache, reducing LLM calls by 12.9% without reducing accuracy. ### Source excerpt arXiv:2610.37826v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) lets a large language model (LLM) answer questions by retrieving relevant passages from a large corpus. However, RAG struggles with multi-hop questions, which require chaining facts spread across several documents through intermediate entities that the question never mentions. Graph-based RAG methods retrieve once from the entities a question names and so rarely recover the full evidence chain. Iterative RAG methods recover it step by step, but their accuracy is limited by the many tasks they ask the LLM to perform, such as planning the sub-questions, deciding their dependencies, rewriting them as answers arrive, and keeping track of what is known. We propose DeFT-RAG, a RAG framework built on three components. First, DeFT-RAG generates one self-contained sub-question at a time from the original question and the sub-questions answered so far with their answers (QA pairs). Each sub-question names every entity it asks about, which removes the need to plan, track dependencies or rewrite, and lets the LLM focus on a single fine-grained task at a time. Second, DeFT-RAG moves the remaining LLM work outside the LLM: rule-based updates to a QA table that keeps track of what is known, and yes/no decisions made by a fine-tuned lightweight decision model. Third, DeFT-RAG caches the QA pairs and reuses an answer when a later question asks a sub-question with the same meaning. On MuSiQue, 2WikiMultiHopQA and HotpotQA, DeFT-RAG achieves the best accuracy among thirteen baselines with both gpt-4o-mini and Qwen3.6 as the backbone, while giving the LLM fewer input tokens than any other iterative method. With a QA cache shared across 1,000 MuSiQue questions, 23.1% of sub-questions are answered from the cache, which reduces LLM calls by 12.9% without lowering accuracy. ## The Frankenstein Table DevFeed: [The Frankenstein Table]() Original publisher: [Read original article]() Author: Preethi Published: 2026-10-29T13:47:08Z Content type: article Language: en Sources: [CSS-Tricks]() Topics: [CSS](), [layout](), [Front end]() Tags: [articles](), [assistive-technology](), [block](), [browser](), [browsers](), [contents](), [css](), [css-grid](), [display](), [flexbox](), [gap](), [grid](), [html](), [html-elements](), [keyboard](), [layout](), [layouts](), [markup](), [navigation](), [table](), [tables]() ### AI overview The "Frankenstein Table" uses table markup as a readable pseudo-semantic skin while CSS Grid controls layout. In the example, the table becomes a grid, `tbody` uses `display: contents`, and rows and their cells use `display: block` to bypass native table layout. The author says VoiceOver and keyboard navigation still work as with a real table in the browsers considered, while acknowledging the approach looks awkward and raises questions about separating content from presentation. The excerpt also stresses that ordinary HTML tables remain useful for semantic tabular data and assistive technology; Grid is valuable for responsive layout, source-order independence, and positioning. It presents this technique as a layout experiment, not a general replacement for tables. ### Source excerpt A super scary way to go about tables using other layout techniques, like CSS Grid. The Frankenstein Table originally handwritten and published with love on CSS-Tricks. You should really get the newsletter as well. ## Before You Model a Single Table: Data Modelling Questions to Ask for AI Agents in Snowflake DevFeed: [Before You Model a Single Table: Data Modelling Questions to Ask for AI Agents in Snowflake]() Original publisher: [Read original article]() Author: Cesar Segura Published: 2026-10-28T21:01:01Z Content type: article Language: en Sources: [Snowflake Builders]() Topics: [schema design](), [semantic-layer](), [JOIN](), [Data Quality](), [knowledge-engineering](), [business logic](), [AI Architecture]() Tags: [2](), [3](), [about](), [agent](), [agents](), [aggregation](), [ai](), [ai-agent](), [ai-agents](), [analyst](), [architect](), [article](), [business](), [business-analyst](), [data](), [data-modelling](), [generative-ai-tools](), [governance](), [model](), [modelling](), [questions](), [snowflake](), [snowflake-cortex](), [table]() ### AI overview Reliable Snowflake Cortex Agent answers depend on reducing ambiguity across the data model, semantic layer, business definitions and evaluation. The article recommends documenting and testing each fact table's grain, using explicit relationships and appropriate date roles, and exposing a simple query path; it favors star schemas as a starting point while allowing bounded wide tables and more complex models beneath a simplified layer. Semantic Views should be scoped by domain, describe business meaning, and define metrics and filters explicitly. Business Ontology can govern canonical terms and link them to assets, but its documented formulas do not execute: Cortex Analyst uses the Semantic View expression, so both must stay aligned and tested. The article also calls for realistic performance profiling and evaluations covering joins, double counting, ambiguity and access controls. It notes that Business Ontology, its Cortex Agents integration and Cortex Sense were in Private Preview for selected accounts as of October 2026. ### Source excerpt A Principal Architect's guide to structuring tables, semantic views and business context so agents generate reliable SQLQuestions prior to modelling in Snowflake for your agents Every few weeks someone asks me the same thing: "We want a Cortex Agent on top of our data. How should we model it?" The honest answer is that most of the questions behind that sentence are not about the agent. They are modelling questions we should have answered years ago, and an agent now exposes them in public. A business analyst carries a lot of tacit knowledge: which date really means "sale", which orders are cancelled, what "revenue" means for Finance versus Sales. An agent has none of that. It reads your schema, infers, and writes SQL. Every ambiguity becomes an inference, and every inference is a chance for a wrong join, filter or aggregation. In this article we will walk through the six questions I ask teams before they start modelling: grain, shape, semantic views, business meaning, performance and evaluation. We will close with a checklist you can take to your next design session. Index 1. Why agents change the rules 2. Question 1: What is the grain, and does every column mean one thing? 3. Question 2: Star, snowflake or wide table, and how many joins are too many? 4. Question 3: How big should a Semantic View be? 5. Question 4: Who owns the meaning of a metric? 6. Question 5: Does physical performance still matter? 7. Question 6: How will we know it works? The Big Picture Conclusions References About Me 1. Why agents change the rules The first thing I explain is that an agent solution is not one model; it is a stack of layers, each with a different job. Confusing them is the root of most disappointing proofs of concept. Curated physical model: tables, views, facts, dimensions, types, keys and quality tests. It represents the data faithfully and consistently. Semantic View: the executable semantic layer. It describes how to query a domain and how to calculate its metrics: logical ## How to Convert a String to a Number in JavaScript DevFeed: [How to Convert a String to a Number in JavaScript]() Original publisher: [Read original article]() Author: Marko Published: 2026-10-28T20:00:00Z Content type: article Language: en Sources: [Marko Denic Blog]() Topics: [JavaScript](), [concatenation]() Tags: [bigint](), [comparison](), [how-to](), [javascript](), [money](), [number](), [plus](), [string](), [table](), [tutorial](), [web-development]() ### AI overview Use `Number(str)` for a strict conversion of the whole string: it returns `NaN` for trailing units, but turns empty or whitespace-only strings into `0`, so handle those separately and validate with `Number.isFinite()`. Use `parseInt(str, 10)` or `parseFloat(str)` to read a number from the start of text with units; `parseInt` truncates decimals, while `parseFloat` keeps them. Unary plus behaves like `Number()`. The article also advises `array.map(Number)` instead of `array.map(parseInt)`, notes JavaScript's basic parsing does not handle locale grouping separators, and recommends `BigInt` or strings for integers beyond the safe-number range. For money, it suggests integer cents because decimal arithmetic can be inexact. ### Source excerpt Convert a string to a number in JavaScript with Number(), parseInt(), parseFloat() and unary plus. Which one to use, a comparison table of edge cases, and how to validate, handle BigInt and money. ## Deferred Schema Loading in Filament v5.8 DevFeed: [Deferred Schema Loading in Filament v5.8]() Original publisher: [Read original article]() Author: Paul Redmond Published: 2026-10-26T23:33:43Z Content type: release Language: en Sources: [Laravel]() Topics: [lazy loading](), [Laravel Boost](), [Livewire]() Tags: [403](), [actions](), [addition](), [agent](), [agent-skill](), [agent-skills](), [also](), [app](), [argument](), [deferred](), [filament](), [first](), [hooks](), [laravel](), [lifecycle](), [loading](), [news](), [schema](), [table](), [this](), [v5]() ### AI overview Filament v5.8.0 adds `deferLoading()` to render a child schema only when its section, tab, wizard step, repeater item, or builder block is reached, reducing initial rendering of content users may not open. Other changes include session-persisted table grouping, configurable default grouping direction, and trait-specific lifecycle hooks that run after a page's own hook. The release also adds stat placeholders, reusable multi-factor challenges, editor height limits, and format-specific export downloaders. Custom downloaders can return a response or redirect to a temporary remote URL, offering an alternative where streamed downloads are blocked by some hosts. The excerpt notes that empty multi-factor schemas validate successfully, so enabled providers should be checked again before validation. These details cover the supplied excerpt. ### Source excerpt Filament v5.8 adds deferLoading() for sections, tabs, wizards, and repeater items, plus session-persisted table grouping and lifecycle hooks in traits. The post Deferred Schema Loading in Filament v5.8 appeared first on Laravel News. Join the Laravel Newsletter to get Laravel articles like this directly in your inbox. ## Cycle-of-Thought: Self-Evaluative Iterative Reasoning for Table Visual Question Answering in Document Images DevFeed: [Cycle-of-Thought: Self-Evaluative Iterative Reasoning for Table Visual Question Answering in Document Images]() Original publisher: [Read original article]() Author: Somraj Gautam, Gaurav Harit Published: 2026-10-26T04:00:00Z Content type: article Language: en Sources: [arXiv: Artificial Intelligence]() Topics: [llm-reasoning](), [Enhancing AI model performance](), [computational complexity](), [Artificial Intelligence]() Tags: [abstract](), [analysis](), [annotation](), [benchmarks](), [cs-ai](), [cs-cl](), [cs-cv](), [data-efficiency](), [errors](), [feedback](), [fine-tuning](), [framework](), [generator](), [hallucinations](), [images](), [improvement](), [inference](), [labeling](), [language](), [language-models](), [lightweight](), [reasoning](), [table]() ### AI overview Self-RefineVQA improves table visual question answering through inference-time self-correction: one vision-language model iteratively generates an answer, critiques it, and refines it through prompt-guided re-evaluation. The abstract reports consistent accuracy gains on tabular VQA benchmarks for monolingual and cross-lingual tasks, with especially strong gains for smaller models (2B-7B parameters), which it says can rival larger counterparts. The method requires no labeled training data or model updates, avoiding the annotation burden of supervised fine-tuning, but increases inference cost linearly. These claims are based on the abstract; benchmark details and quantitative results are not provided. ### Source excerpt arXiv:2610.32423v1 Announce Type: cross Abstract: Recent advances in Vision-Language Models (VLMs) have significantly improved Visual Question Answering (VQA) performance, yet their reasoning over structured content such as tables remains inconsistent, often plagued by spatial misalignment and logical hallucinations. Existing approaches for improving table VQA typically rely on architectural modifications or expensive supervised fine-tuning (SFT), which impose high barriers to entry due to the need for large-scale, expert-annotated datasets and significant computational resources. In this work, we propose Self-RefineVQA, a training-free, inference-time self-correction framework that improves table VQA performance without additional labeled data or model updates. Inspired by human revision behavior, our method enables a single VLM to iteratively act as a generator, critic, and refiner, forming a closed self-feedback loop that corrects common reasoning errors through prompt-guided re-evaluation. Experiments on tabular VQA benchmarks demonstrate that Self-RefineVQA yields consistent accuracy gains in monolingual and cross-lingual understanding, with a particularly significant boost for small models, enabling lightweight models (e.g., 2B-7B parameters) to rival the performance of significantly larger counterparts. Furthermore, we provide a comparative complexity analysis demonstrating that while inference cost increases linearly, our framework offers superior data efficiency and a lower cumulative cost by eliminating the prohibitive annotation bottleneck of human labeling required for fine-tuning. Our results highlight the potential of prompt-based self-improvement as a scalable, accessible paradigm for reliable table reasoning in VLMs. ## Enhancing Table Recognition with Vision LLMs: A Benchmark and Neighbor-Guided Toolchain Reasoner DevFeed: [Enhancing Table Recognition with Vision LLMs: A Benchmark and Neighbor-Guided Toolchain Reasoner]() Original publisher: [Read original article]() Author: Yitong Zhou, Mingyue Cheng, Qingyang Mao, Jiahao Wang, Feiyang Xu, Xin Li Published: 2026-10-26T04:00:00Z Content type: article Language: en Sources: [arXiv: Artificial Intelligence]() Topics: [vlm](), [Artificial Intelligence](), [image recognition](), [Image processing](), [AI Foundation Models]() Tags: [abstract](), [approach](), [arxiv](), [based](), [benchmark](), [bridge](), [capabilities](), [content](), [cs-ai](), [cs-cv](), [datasets](), [design](), [drawing](), [enhancing](), [evaluations](), [experience](), [llms](), [recognition](), [table](), [toolchain](), [vision](), [with]() ### AI overview The proposed hierarchical benchmark evaluates Vision LLM recognition of unstructured table structure and content in training-free settings. Its evaluations identify low-quality images as a major bottleneck. The authors introduce Neighbor-Guided Toolchain Reasoner (NGTR), which applies tool-selection experience from a similar input and uses a reflection module to supervise lightweight visual-tool invocation. Experiments on public datasets reportedly show improved recognition over vanilla VLLMs; the excerpt provides no quantitative results or tool-specific comparisons. ### Source excerpt arXiv:2412.20662v4 Announce Type: replace-cross Abstract: Pre-trained foundation models have recently made significant progress in table-related tasks such as table understanding and reasoning. However, recognizing the structure and content of unstructured tables using Vision Large Language Models (VLLMs) remains under-explored. To bridge this gap, we propose a benchmark based on a hierarchical design philosophy to evaluate the recognition capabilities of VLLMs in training-free scenarios. Through in-depth evaluations, we find that low-quality image input is a significant bottleneck in the recognition process. Drawing inspiration from this, we propose the Neighbor-Guided Toolchain Reasoner (NGTR) framework, which is characterized by integrating diverse lightweight tools for visual operations aimed at mitigating issues with low-quality images. Specifically, we transfer a tool selection experience from a similar neighbor to the input and design a reflection module to supervise the tool invocation process. Extensive experiments on public datasets demonstrate that our approach significantly enhances the recognition capabilities of the vanilla VLLMs. We believe that the benchmark and framework could provide an alternative solution to table recognition. [Next page]()