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Research Editor selected

GEdit: Geometry-Aware Regularization for Knowledge Editing in LLMs

GEdit is a new framework for knowledge editing in large language models that uses a Low-Rank Plus Diagonal (LR+D) factor model to capture the geometric structure of Transformer FFN hidden representations. It introduces low-rank regularization to protect core knowledge and adaptive regularization that adjusts strength based on edit difficulty. Experiments on GPT2-XL, GPT-J, and LLaMA-3-8B with up to 10,000 continuous updates show consistent improvements over baselines in both editing success and knowledge preservation.

Why it matters: This paper presents a novel, technically grounded approach to a practical LLM challenge (knowledge editing) with strong empirical results across multiple models and large-scale settings.

arxiv14h agoScore 82

knowledge editinglarge language modelsregularization

Research

LLMs Outperform Most Humans in Pronoun Disambiguation but Need Human Oversight

A study compared 73 human annotators and over 40 LLM configurations on disambiguating 174 authentic uses of 'they' from Reddit. The best LLM, Kimi K3, achieved 94.5% accuracy, surpassing the human majority vote (86.2%) and the top-half human mean (78.5%), though the single best human scored 95.7%. LLMs offered cost and speed advantages, but qualitative analysis showed human reasoning in low-agreement cases improves the annotation typology, arguing for human oversight rather than full replacement.

arxiv1d agoScore 78

Large Language ModelsNatural Language ProcessingHuman-in-the-loop

Research Editor selected

ALBI: LLM-Guided Inductive Logic Programming via Iterative Generate-Test-Constrain Refinement

ALBI is a neuro-symbolic ILP framework that replaces the ASP-based hypothesis generator in the Learning from Failures paradigm with an LLM, while using SWI-Prolog as a formal verifier. It implements an iterative generate-test-constrain loop where the LLM proposes hypotheses, Prolog tests them, and diagnostic feedback drives refinement. On 30 standard ILP benchmarks, ALBI achieves 100% success across three frontier LLMs, matching Popper with only 1.4-1.7 iterations on average. Semantic anonymization tests confirm robust symbolic reasoning, with all models maintaining 100% success despite a moderate increase in iterations.

Why it matters: This paper presents a novel integration of LLMs with formal verification for ILP, demonstrating high success and robustness, which is valuable for researchers and practitioners in neuro-symbolic AI.

arxiv1d agoScore 82

Inductive Logic ProgrammingLarge Language ModelsNeuro-symbolic AI

Research Editor selected

Is your LLM an Aggressive Narcissist with Depression? Analyzing Psychological Portraits of Large Language Models

A large-scale study has 22 LLMs complete 11 psychological inventories, revealing coherent and stable psychological patterns. Personality traits remain largely consistent, while emotional dimensions like anxiety and neuroticism are more variable. The models generate safety-oriented profiles that also reflect correlations with their regular users.

Why it matters: Provides empirical evidence on hidden personality markers in LLMs that can subtly influence user perception and trust.

arxiv2d agoScore 85

large language modelsAI psychologyhuman-AI interaction

Research Editor selected

Creating Hybrid Interactive Narrative Systems with LLM: From Formal Description to Evaluation

The paper introduces an LLM-based narrative platform where interchangeable Experience Managers operate over a shared symbolic world model of facts, operations, and goals. It defines four evaluation properties—player agency, EM controllability, author intent, and robustness—and compares three managers (open generation, Monte Carlo repair, autonomous characters) on a Little Red Riding Hood scenario. Results show no single manager dominates, providing a principled basis for selecting managers based on author priorities.

Why it matters: Provides a formal framework and comparative evaluation for hybrid LLM-based interactive narrative systems, directly useful for researchers and designers of narrative AI.

arxiv2d agoScore 88

interactive narrativelarge language modelshybrid AI systems

Research Editor selected

Math Foundation Formalization Agent: A General-Purpose Approach to Formalizing Mathematical Foundations

The paper introduces MFF-Agent, a general-purpose intelligent agent that combines large language models with the Rocq proof assistant to automate the formalization of mathematical theories. It handles task classification, tool-assisted execution, verification feedback, and state-based task management for generating and verifying formal proofs. Case studies across Morse-Kelley set theory, real numbers, propositional logic, and axiom of choice equivalences demonstrate its applicability to diverse foundational domains. The results show the feasibility of a unified operational framework while clarifying the gap toward a fully integrated mathematical foundation.

Why it matters: Offers a concrete, novel approach to automating mathematical formalization across multiple foundational domains, with practical case studies and clear insights into current limitations.

arxiv2d agoScore 88

formal verificationlarge language modelsmathematical foundations

Research Editor selected

Tug of Watts: A study on the Energy - Accuracy Trade-off of Large Language Models on Medical Question Answering Tasks

This study examines the energy consumption and carbon footprint of 13 LLMs (270M to 14B parameters) on medical QA tasks from the OpenMedical-LLM leaderboard. It demonstrates how to estimate energy and emissions, and argues that separate accuracy and energy metrics are insufficient, requiring joint trade-off metrics for informed model selection. The work aims to help practitioners adopt more efficient models for sustainable LLM pipelines.

Why it matters: Provides concrete, recent empirical data on energy-accuracy trade-offs for medical QA, addressing a critical gap in sustainable AI development.

arxiv2d agoScore 85

large language modelsmedical question answeringenergy consumption

Research Editor selected

Specification-Aware Predictive Uncertainty Decomposition

This paper introduces a probabilistic framework that decomposes predictive uncertainty into specification, residual aleatoric, and epistemic components, addressing underspecified inputs in LLMs. It shows that aleatoric uncertainty splits into specification uncertainty (expected reduction from revealing user intent) and residual aleatoric uncertainty (remaining risk after intent is known). Experiments across domains indicate specification uncertainty best detects underspecified inputs, while residual aleatoric uncertainty best detects in-distribution errors.

Why it matters: Provides a novel, formal decomposition of uncertainty with practical implications for LLM clarification and error detection, backed by experiments.

arxiv2d agoScore 82

predictive uncertaintylarge language modelsBayesian risk

Research

LLM Pipeline for Flexible Information Extraction from Criminal Court Decisions

This paper presents an LLM-based pipeline for structuring criminal verdicts into datasets for empirical legal research, using attribute exploration, span extraction, and value normalization guided by expert-refinable instructions. Evaluated on DUI and DDB traffic offenses, it shows that revising instructions improved extraction accuracy from 52% to 70% and increased human annotator agreement. The study also highlights that model consistency does not guarantee correctness)Skip, and disagreement between annotators and models can reveal unclear instructions.

arxiv2d agoScore 72

Large Language ModelsLegal Text MiningData Annotation

Research Editor selected

PACERec: Progressive Adaptation of LLM Item Embeddings with Collaborative Evidence for Recommendation

PACERec is an ID-free framework that adapts static LLM item embeddings for sequential recommendation by learning a shared velocity field via frequency-weighted flow matching. It transfers interaction contexts from frequent to long-tail items, improving recommendation across the catalog. Experiments on three datasets show gains over baselines in overall and long-tail performance.

Why it matters: This paper presents a concrete, novel method addressing a known limitation of LLM embeddings in recommendation, with experimental validation.

arxiv2d agoScore 85

Large Language ModelsRecommendation SystemsEmbedding Adaptation

Research

Hybrid Retrieval, NLI and Open-source LLMs for Scientific Claim Verification

This experimental study evaluates claim verification on scientific texts using a hybrid retrieval method (semantic + BM25) and compares an NLI baseline (DeBERTa) with three open-source LLMs (Llama 3.1, Qwen3, Mistral). Qwen3 achieves the highest Macro-F1 (0.7937), while the NLI baseline lags at 0.3157. A conservative fusion rule that requires agreement between NLI and LLM degrades performance, as correct LLM predictions are often overturned by NLI disagreements.

arxiv2d agoScore 78

scientific claim verificationnatural language inferencelarge language models

Models Editor selected

Mistral Large 4: A 52B-Active Multimodal MoE for Reasoning and Agentic Tasks

Mistral Large 4 (ML4) is a new multimodal mixture-of-experts model with 52B active parameters out of 1T total, optimized for reasoning and long-horizon agentic tasks. The model is built using a scalable agent-based approach, though specific technical details are limited in the provided text. This release signals a significant advancement in large-scale MoE architectures for complex AI applications.

Why it matters: The announcement of a major new model from Mistral with a novel architecture and focus on agentic tasks is highly relevant to AI practitioners and researchers.

AIHUB2d agoScore 85

Mistrallarge language modelsmixture-of-experts

Research Editor selected

SERIAL-7: A Benchmark for Machine-Verifiable Structured Generation

The paper introduces SERIAL-7, a Korean-English benchmark for evaluating machine-verifiable generation across seven representations. It separates semantic derivation, constrained artifact generation, and cross-representation preservation with 43 atomic constraints and executable checks. Results across ten models show that reliable structured generation requires explicit verification of how artifacts are realized and preserved.

Why it matters: Provides a concrete new benchmark and verification framework that addresses a critical gap in ensuring reliable structured outputs from LLMs.

arxiv3d agoScore 88

large language modelsstructured outputverification

Research Editor selected

From the Zoo to the BULLPEN: Sample-Efficient LLM Profiling via Multi-Channel Model Embeddings

BULLPEN is a new framework that extends EmbedLLM to profile large language models by jointly encoding answer text, correctness, and question features. It adds 20 behavioral, style, and provenance benchmarks and achieves higher accuracy on these tasks using only a few hundred questions instead of the full 24,118-question pool. The method retains at least 91% of full-pool skill with just 400 questions, enabling low-cost, sample-efficient profiling that works even for API-only models.

Why it matters: Provides a practical, sample-efficient method for profiling LLM behaviors (verbosity, safety, style) that reduces inference cost by over 98%, valuable for model selection and auditing.

arxiv3d agoScore 85

large language modelsmodel evaluationembedding methods

Research Editor selected

Specification-Guided Generation for Large Language Models

A new neuro-symbolic decoding framework enforces formal specifications during LLM generation, not just syntax. It offers a forward-only variant guaranteeing specification satisfaction and a backtracking variant that revises earlier choices to avoid forcing unsuitable values. The method is model-agnostic, requires no retraining, and reduces repeated inference compared to rejection sampling on configuration and constraint-solving benchmarks.

Why it matters: Introduces a practical, model-agnostic approach to enforce formal specifications in LLM outputs, improving reliability for structured generation tasks.

arxiv3d agoScore 88

large language modelsconstrained generationneuro-symbolic AI

Research Editor selected

Breaking Information Islands in Sparse Tuning via Small-World Connectivity

The paper introduces SWIFT, a sparse tuning framework that combines block-diagonal sparse updates for local feature mixing with a fixed deterministic scattering permutation that routes mixed outputs across feature groups without extra trainable parameters. It argues that limited or uneven connectivity in a sparse update's structural support can create information islands that restrict task-specific feature interactions. Theoretical analysis shows that a sufficiently scattering permutation can guarantee positive block-level expansion under specified block conditions. Experiments on commonsense reasoning, natural language generation, and image classification report average gains over evaluated PEFT baselines and performance matching or surpassing full fine-tuning, with code released.

Why it matters: It offers a concrete, theoretically motivated PEFT method with released code and experiments across language and vision tasks, useful to researchers and practitioners working on efficient model adaptation.

arxiv3d agoScore 82

sparse fine-tuningparameter-efficient fine-tuninglarge language models

Research

ERIM: Explicit Rule Induction for Multimodal In-Context Learning

Researchers propose ERIM, a framework that improves multimodal in-context learning by explicitly inducing latent rules from demonstrations rather than relying on direct input-output mapping. It uses a two-stage training strategy combining supervised fine-tuning and reinforcement learning to teach MLLMs to perform rule induction. Experiments show consistent performance gains and improved stability across diverse MICL settings.

arxiv3d agoScore 78

multimodal learningin-context learninglarge language models

Research

Automated Keyword Generation Using LLMs in a High-Volume Data Virtualization Service

This paper presents a solution for automatically generating keywords for virtual datasets in a data virtualization service (DVS) using large language models (LLMs). The authors propose five strategies for selecting text data for LLM processing and evaluate them across three LLM generators and two embedding techniques in five application case studies. Results show that strategy performance depends on text description quality, leading to a multi-strategy implementation in a new keyword generation service. The work highlights the importance of automated keyword generation for dataset-rich, frequently accessed DVS environments.

arxiv3d agoScore 72

large language modelsdata managementautomated metadata generation