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For large language models (LLMs), reasoning over graphs can help solve many problems. Prior work has tried to improve LLM graph reasoning through different training methods, but the merits of such approaches remain unclear and the limitations of each approach with respect to generalizability of reasoning are often not thoroughly explored. In this paper we systematically compare the ability of LLMs to learn fundamental graph tasks across a variety of training methods and their ability to generalize out of distribution across various dimensions. We highlight key tradeoffs between training methods, e.g., training specialized graph encoders and fusing their embeddings with LLMs consistently collapses in terms of generalizability; however, no single method shows clear superiority across all dimensions of generalizability, regardless of the size of the model.

ACL 2026

Generalization in Graph Reasoning: A Systematic Comparison of LLM Training Approaches

llm benchmarking

graph reasoning

large language models

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Self-correction—the ability of LLMs to detect and fix their own errors—has been studied extensively for mathematical and code reasoning, with limited prior work on table reasoning (primarily multi-agent pipelines such as Table-Critic, ACL 2025, rather than single-model structured prompting). Tables present unique challenges: errors arise from wrong cell retrieval, incorrect computation, flawed logic, and hallucination of values not present in the data. We conduct the first cross-provider single-model self-correction analysis for table reasoning across five providers (Google, Moonshot AI, Zhipu, Alibaba, MiniMax), testing five models (Gemini 3.1 Pro, Kimi K2.5, GLM 5, Qwen 3.5+, MiniMax M2.5) on WikiTableQuestions and TabFact with a multi-seed paired protocol. We propose Structured Self-Correction (SSC), a table-specific verification chain that guides models through cell verification, computation checking, logic validation, and completeness assessment. We confirm that the Accuracy-Correction Paradox (terminology from Li 2025) previously observed in math extends to tables: models with base accuracy in the mid-60s–mid-70s region benefit modestly from self-correction (multi-seed mean SCG up to +1.3% with within-seed point estimates as high as +3.4%), while stronger models above this region are systematically harmed by over-correction (multi-seed mean SCG down to -1.3%, with 95% bootstrap CIs significantly below zero). SSC reduces over-correction rates in 9 of 10 conditions, with reductions of 38–69% on TabFact. An inference-mode-controlled probe shows that SSC's qualitative direction is robust for Qwen 3.5+ across reasoning-ON and reasoning-OFF settings, while GLM 5 exhibits a substantial mode-dependent shift, indicating that mode robustness itself is model-dependent. Stronger baselines (self-consistency, self-critic, tool-augmented arithmetic verification, majority voting, and a same-family scaling probe) further characterize where SSC helps. Ablation studies reveal that answer-aware review is essential, reasoning traces aid error detection, and iterative correction shows diminishing returns. A FinQA domain transfer probe confirms a capability floor: self-correction fails when base task competence is very low (21.5% accuracy). Our primary contribution is empirical: we characterize the conditions under which self-correction helps or harms table reasoning, providing actionable guidance for practitioners.

Can LLMs Self-Correct Table Reasoning Errors?

Recent advances in text-to-SQL have shown that methods such as Group Relative Policy Optimization (GRPO) can substantially improve reasoning performance, but these approaches remain inherently on-policy, limiting their ability to incorporate novel reasoning patterns. In this work, we address this limitation by leveraging existing datasets to generate high-quality off-policy rollouts, enabling mixed-policy training that exposes models to diverse and informative reasoning trajectories. We present the first application of mixed-policy GRPO to the text-to-SQL domain and introduce a systematic study of off-policy data generation strategies for this setting, including a novel method, Iterative Error Correction (IEC), which iteratively refines model outputs through targeted feedback. Our experiments show that mixed-policy GRPO outperforms both base models and on-policy GRPO, yielding average improvements of +4.7% over base models and +4.1% over on-policy GRPO across the Spider and BIRD benchmarks. Gains are particularly strong on BIRD, reaching up to +7.3% over base models and +4.5% over on-policy GRPO.

Mixed-Policy GRPO for Text-to-SQL with Off-Policy Data Generation

Large language models (LLMs) are increasingly evaluated on structured data tasks—table question answering, chart comprehension, graph reasoning, and time series analysis—yet these benchmarks operate in isolation. We ask: does competence in one structured data format predict competence in another when the underlying data and questions are identical? We construct a controlled benchmark of 1,724 programmatically generated questions over 250 sub-tables drawn from 10 datasets spanning 7 domains. Each sub-table is rendered in five formats: Markdown table, chart image, chart text-description, entity-relationship graph, and unlabeled time series. We evaluate six frontier March 2026 LLMs and measure cross-format transfer via tetrachoric correlation on binary (correct/incorrect) outcomes. Our key findings: (1) structured data comprehension skills transfer strongly across text-based formats, with mean tetrachoric r=0.84 between table, graph, and time series representations (r=0.85 overall; r=0.84 for non-ceiling models, confirming the result is not an artifact of high accuracy); (2) chart text-descriptions show consistently lower transfer (r=0.65) despite being length-matched to tables; (3) chart image comprehension is largely siloed, with a 20-50% accuracy gap compared to equivalent text descriptions; and (4) enabling chain-of-thought reasoning improves accuracy by 24-38% for DeepSeek V3.2 but only 0-0.5% for Gemini 3.1 Pro, suggesting reasoning benefits may vary substantially across models. On hard questions (multi-hop and conditional aggregation), transfer remains substantial at r=0.78.

Do Structured Data Comprehension Skills Transfer Across Representation Types? A Systematic Study with Frontier LLMs

Deployed LLM agents rely on textual policies that must evolve from operational experience, yet over long horizons flat-text representations make verification increasingly difficult to sustain as policies grow. We propose Graph-Regularized Agentic Context Evolution (GRACE), which represents policy content as a typed semantic graph and performs scoped structural validation in the local neighborhood of modified nodes, rather than through document-wide consistency analysis. We evaluate GRACE within a fixed operational harness on the telecom domain of $\tau^2$-bench under a controlled distribution-shift protocol. GRACE improves strict reliability (pass^3) from 0.227 to 0.833 and is the only method that sustains improvement over the full trajectory. The three baselines each exhibit a different failure mode, revealing that contradiction avoidance alone is insufficient. Our results suggest that sustained policy evolution may require both a structural substrate for scoped verification and an active mechanism that consolidates accumulated knowledge.

Graph-Regularized Agentic Context Evolution

Large Language Models (LLMs) are increasingly used to generate structured outputs—JSON objects, SQL queries, and structured records—from formal schemas. While recent advances in constrained decoding and schema-aware prompting have improved syntactic compliance, the semantic reliability of these outputs remains poorly characterized. We investigate this gap through the lens of schema drift—the inevitable evolution of database schemas in production environments through column renamings, type changes, and constraint modifications.

We introduce StructHallu-Drift, a benchmark and evaluation framework for studying structured hallucinations under schema evolution. We contribute: (1) a six-category hallucination taxonomy that disentangles syntactic validity from semantic fidelity; (2) a controlled evaluation suite applying realistic schema mutations at three severity levels to established NL-to-structure datasets; and (3) a systematic evaluation of four LLMs spanning 7B to 70B parameters across three structured output tasks.

Experiments on 1,200 schema–model evaluation instances reveal four key findings: (i) 39–54% of structured outputs contain at least one semantic hallucination; (ii) schema drift severity has surprisingly minimal effect on hallucination rates (∼44% across all levels, p = 0.59), suggesting imperfect schema conditioning under our prompting setup; (iii) output format is the dominant factor in generation reliability, with SQL achieving ∼85% semantic validity while schema-grounded record generation drops to 7–24%; (iv) each model exhibits a distinct hallucination fingerprint, implying that mitigation strategies must be model-specific rather than universal. We publicly release our benchmark and evaluation toolkit.

StructHallu-Drift: Benchmarking Structured Hallucinations Under Schema Evolution in LLMs

Recent advances in large language models (LLMs) have significantly enhanced automated legal reasoning. Yet, it remains unclear whether their performance reflects genuine legal reasoning ability or artifacts of data contamination. We present a comprehensive empirical study of tax law reasoning approaches and implement a contamination detection protocol to rigorously assess LLM reliability. We show that performance can be inflated by contamination. Building on this analysis, we conduct a systematic evaluation, comparing monolithic LLMs with hybrid systems that translate statutory text into formal representations and delegate inference to symbolic solvers. We build a novel test suite designed to probe generalization to unseen documents via case and rule variations. Our findings indicate that legal reasoning is inherently compositional and that neuro-symbolic frameworks offer a more reliable and robust foundation for legal AI, as well as improved generalization to unobserved situations.

Reasoners or Translators? Contamination-aware Evaluation and Neuro-Symbolic Robustness on Tax Law

Dense retrievers powered by pretrained embeddings are widely used for document retrieval but struggle in specialized domains due to the mismatches between the training and target domain distributions. Domain adaptation typically requires costly annotation and retraining of query-document pairs. In this work, we revisit an overlooked alternative: applying PCA to domain embeddings to derive lower-dimensional representations that preserve domain-relevant features while discarding non-discriminative components. Though traditionally used for efficiency, we demonstrate that this simple embedding compression can effectively improve retrieval performance. Evaluated across 9 retrievers and 14 MTEB datasets, PCA applied solely to query embeddings improves NDCG@10 in 75.4\% of model-dataset pairs, offering a simple and lightweight method for domain adaptation.

More Than Efficiency: Embedding Compression Improves Domain Adaptation in Dense Retrieval

Recent advances in large language models (LLMs) have greatly improved Text-to-SQL performance for single-table queries. But, it remains challenging in multi-table databases due to complex schema and relational operations. Existing methods often struggle with retrieving the right tables and columns, generating accurate JOINs and UNIONs, and generalizing across diverse schemas. To address these issues, we introduce UNJOIN, a two-stage framework that decouples the retrieval of schema elements from SQL logic generation. 
In the first stage, we merge the column names of all tables in the database into a single-table representation by prefixing each column with its table name. This allows the model to focus purely on accurate retrieval without being distracted by the need to write complex SQL logic. In the second stage, the SQL query is generated on this simplified schema and mapped back to the original schema by reconstructing JOINs, UNIONs, and relational logic. Evaluations on SPIDER and BIRD datasets show that UNJOIN matches or exceeds the state-of-the-art baselines. UNJOIN uses only schema information, which does not require data access or fine-tuning, making it scalable and adaptable across databases. Our code is available at: https://github.com/coral-lab-asu/unjoin

UNJOIN: Enhancing Multi-Table Text-to-SQL Generation via Schema Simplification

Large Language Models (LLMs) often underperform in domain adaptation for industrial settings, where available corpora are limited and structurally diverse. These corpora frequently include non-natural formats such as tables, entity lists, or bullet-point instructions that hinder effective learning. To understand and improve domain adaptive pretraining on such data, we introduce SParK-Eval (Structure-aware Parametric Knowledge Evaluation), a framework that constructs question–answer pairs from pretraining data and annotates each with its input structure (e.g., natural sentence, table, list). This enables fine-grained analysis of how input structure affects parametric knowledge acquisition during DAPT. Additionally, we propose a prompt-based input normalization method that converts diverse inputs into coherent natural sentences, providing a reference for isolating structural effects. Our experiments show that LLMs acquire substantially more knowledge from natural sentences than from their structurally non-standard counterparts. These findings underscore the importance of structure-aware evaluation in diagnosing learning challenges and guiding effective domain adaptation strategies.

SParK-Eval: Evaluating Structure-Aware Knowledge Acquisition in LLMs for Domain Adaptation to Industrial Records

We introduce SciTaRC, an expert-authored benchmark for question answering over scientific tables that targets composite, multi-step reasoning. To enable fine-grained diagnostic analysis beyond end-task accuracy, SciTaRC pairs each question with a manually constructed reasoning plan and explicit complexity metrics. State-of-the-art models fail on at least 23\% of these questions, while highly capable open-weight models like Llama-3.3-70B collapse on 65.5\% of the benchmark. Error analysis shows that, in zero-shot settings, failures are driven primarily by question comprehension, where models misinterpret the scientific query and derive the wrong reasoning objective. To determine whether overcoming this gap is sufficient, we use structured plans to decouple strategy formulation from execution. Surprisingly, providing step-by-step oracle plans yields only limited gains and fails to eliminate the performance gap. This reveals a substantial execution bottleneck: both natural language and code-based methods struggle to reliably carry out long-horizon computational chains over structured data. Ultimately, SciTaRC serves as a rigorous diagnostic testbed for studying both planning and execution in scientific table reasoning.

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