Search: “llm”

CritICL: Inference-Time Weak-to-Strong Generalization from Small Language Model Failure Modes

arXiv:2608.27455v1 Announce Type: new Abstract: Recent advances in inference-time scaling have significantly improved the reasoning performance of large language models (LLMs). However, these methods

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Research

Discovering Relationships in Data Lakes Using Large Language Models: An Industrial Case

DGX agent

arXiv:2608.26750v1 Announce Type: new Abstract: Data lakes rely on metadata to remain usable, yet this meta data is often limited or weakly informative for column relationship discovery, especially in

researcharxiv-cs-ai

28 Aug 2026

Do Coding Agents Need Executable World Models, Simplification, and Verification to Solve ARC-AGI-3?

arXiv:2607.15439v2 Announce Type: replace Abstract: Our previous ARC-AGI-3 agent bundled executable world modeling, prompted simplification, and exact replay verification, leaving their individual con

Do Language Models Follow Occam's Razor? An Evaluation of Parsimony in Inductive and Abductive Reasoning

arXiv:2509.03345v3 Announce Type: replace Abstract: Non-deductive reasoning, encompassing inductive and abductive reasoning, is essential in addressing complex real-world questions. One key feature of

agentsarxiv-cs-ai

Double Trouble: Bilingual Pretraining Leaves Language-Conditioned Effects in Shared-Language Representations

arXiv:2608.26576v1 Announce Type: new Abstract: When researchers compare multilingual models for probing, interpretability, or cross-lingual transfer, they often align embedding spaces and assume that

DuMateBench: Evaluating Autonomous Agents in Complex Real-World Workflows

arXiv:2608.26546v1 Announce Type: new Abstract: Autonomous agents are increasingly adopted to complete complex, multi-tool workflows in real-world settings. However, existing benchmarks typically sepa

EEG-to-Report: An Annotation and Feature-Text Framework for Training Language Models on Clinical EEG

arXiv:2608.26153v1 Announce Type: new Abstract: Clinical electroencephalography (EEG) reporting remains largely manual and time-consuming, and current EEG software ecosystems do not produce the struct

researcharxiv-cs-ai

ElementCheck: Complexity-Aware Long-Form Text Factuality Evaluation via Sentence Elements

arXiv:2608.26118v1 Announce Type: new Abstract: Existing long-form factuality evaluation relies on the decompose-retrieve-verify pipeline. However, the pipeline suffers from noise from claim decomposi

Emotional Preferences as Goal-Priority Regulation

arXiv:2608.27072v1 Announce Type: cross Abstract: A core question in decision-making for agents is whether the relative priorities of competing lower-level objectives can be determined by emotional pr

Evaluating Language Models in Realistic Conversational Contexts

arXiv:2608.26131v1 Announce Type: new Abstract: As Large Language Models (LLMs) are increasingly deployed to serve open-ended, multi-turn interactions, evaluating conversational quality at human scale

Fine-Tuning of Transformer models with Frames

arXiv:2608.26430v1 Announce Type: new Abstract: Parameter-Efficient Fine-Tuning (PEFT) strategies such as Low-Rank Adaptation (LoRA) are effective solutions for fine-tuning large-scale pre-trained mod

FIRSTPASS: A Multi-Domain, Multi-Round Peer Review Dataset Grounded in Real Editorial Outcomes

arXiv:2608.26129v1 Announce Type: cross Abstract: Scientific peer review datasets have trained AI systems exclusively on Computer Science and Machine Learning venues, producing models that critique ab

How Unlikely Is 'Unlikely'? Assessing Verbal Probability Perception Across Large Language Models

arXiv:2608.26327v1 Announce Type: cross Abstract: Large language models increasingly produce and interpret verbal probability expressions, yet whether these expressions carry consistent meaning across

Making Clinical Language Models Auditable: Concept-Guided Fine-Tuning for Robust Prediction

arXiv:2608.27397v1 Announce Type: cross Abstract: Clinical language models can achieve strong in-hospital accuracy yet fail under deployment shifts because they exploit note-specific artifacts (e.g.,

researcharxiv-cs-ai

MENTOR: Reinforcement Learning via Flexible Teacher-Optimized Rewards for Tool-Use Distillation

arXiv:2510.18383v4 Announce Type: replace-cross Abstract: Distilling the tool-use capabilities of large language models (LLMs) into small language models (SLMs) is essential for their practical applic

Methodological and Conceptual Framework for 5D Multi-Table Analysis: A Unified Approach for Complex Data Reuse

arXiv:2608.26149v1 Announce Type: new Abstract: Multi-table learning remains a major challenge in machine learning for healthcare and other complex information systems. Relational data combine several

Modality Maturity Index: A benchmark for assessing multimodal capabilities of omni models

arXiv:2608.26317v1 Announce Type: cross Abstract: Frontier language models are increasingly marketed as omni systems that can perceive and respond across modalities. Existing evaluation frameworks, ho

Modular Expert Merging for Biomedical Retrieval

arXiv:2602.04731v2 Announce Type: replace Abstract: Adapting general-purpose LLMs into domain-specialized dense retrievers typically requires large-scale training on mixed-domain data. We show that me

Mutual Debiasing via Dual-Seed Comparison for Probabilistic Sampling in Large Language Models

arXiv:2608.26161v1 Announce Type: cross Abstract: Although Large Language Models (LLMs) demonstrate remarkable capabilities in reasoning and decision-making, high-fidelity probabilistic sampling remai

PACE: A Unified Condense-and-Extract Paradigm for Fast VLM Inference

arXiv:2608.27206v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) demonstrate exceptional visual reasoning capabilities, yet their inference costs escalate rapidly with the proliferation

researcharxiv-cs-ai

Pair-Level Essay-Scale Republication and Reuse from Fragmented Historical Text Reuse: A Workflow Study on Eighteenth-Century Books and Newspapers

arXiv:2608.27343v1 Announce Type: new Abstract: This paper addresses the recovery of essay-scale republication and reuse from fragmented text-reuse evidence, a setting whose central challenge is pair-

Predicting Quantifiability from Primary Screens to Prioritize Dose-Response Profiling

arXiv:2608.26538v1 Announce Type: new Abstract: High-throughput drug screening relies on low-cost primary assays to prioritize compounds for more expensive dose-response profiling, where potency is ul

researcharxiv-cs-lg

Privacy Without Regret: Differentially Private Inference-Time Alignment

arXiv:2608.26324v1 Announce Type: new Abstract: Best-of-N (BoN) sampling is the simplest and most widely deployed inference-time alignment strategy, but it suffers from two distinct problems: reward h

Prompt Sensitivity of Generative Agents: Evidence from an Epidemic Model

arXiv:2608.26221v1 Announce Type: cross Abstract: As generative AI gains traction, researchers are investigating its potential to serve as proxies for humans. From undergoing cognitive psychology expe

agentsarxiv-cs-ai

QuantumBoostNet: A Hybrid Classical-Quantum Architecture for Enhanced Accuracy in Cardiac Ultrasound View Identification

arXiv:2608.27302v1 Announce Type: new Abstract: Accurate identification of the correct view or angle in cardiac ultrasound (echocardiogram) is a critical component of cardiologic imaging. This step is

Reward-Informed Sparse Autoencoders and the Solution-Completeness Confound

arXiv:2608.26136v1 Announce Type: new Abstract: Sparse autoencoders (SAEs) decompose language-model activations into sparse, interpretable features, and an appealing way to aim them at reasoning is to

SKILL.state: Scalable Long-Horizon Agent Skills

arXiv:2608.26263v1 Announce Type: new Abstract: Large Language Models (LLMs) increasingly act as autonomous agents executing complex, long-running procedural skills. Existing agent runtimes maintain e

agentsarxiv-cs-ai

Sycophancy Suppression Can Impair Rational Updating: Anti-Sycophancy Should Preserve the Ability to Update

arXiv:2608.26511v1 Announce Type: new Abstract: Large language models often exhibit sycophancy, revising their answers to align with users when users push back. Such answer flips, however, can arise f

Local Ai

The Accuracy-Efficiency Paradox Quantifying Net Energy Loss in on-Device Energy Forecasting

arXiv:2608.26134v1 Announce Type: new Abstract: Energy forecasting aims to maximize accuracy to ensure energy efficiency by reducing energy waste, an objective that applies equally to on-device foreca

local-aiarxiv-cs-ai

Towards a universal meta-optics solver via large language models

arXiv:2608.26417v1 Announce Type: cross Abstract: Metasurface design increasingly requires fast models that can operate across structurally distinct device families, rather than retraining a separate

Towards Expert Financial QA via Self-Improving RAG

arXiv:2608.26706v1 Announce Type: new Abstract: Expert-level financial question answering requires both grounded verification to catch numeric hallucinations and audit trails for regulatory compliance

agentsarxiv-cs-cl

TransMeme: A Multi-Agent Framework for Cross-Cultural Meme Transcreation

arXiv:2608.27127v1 Announce Type: new Abstract: Internet memes are a pervasive form of multimodal online communication; however, such communication often involves users from diverse linguistic and cul

Which Metrics Save the Most Human Annotation? Prediction-Powered Evaluation and Meta-Evaluation

arXiv:2608.26638v1 Announce Type: new Abstract: Across various non-verifiable tasks, human evaluation is reliable but expensive, while automatic metrics are more scalable but often biased. Building on

Activation Steering Transfer to Agents: One Gain Ratio Does Not Identify Potency and Efficacy

arXiv:2607.09156v2 Announce Type: replace Abstract: Additive activation steering is calibrated in single-turn chat and then deployed inside agent scaffolds. The quantity usually reported for that move

safetyarxiv-cs-lg

AI/ML Life Cycle Management for Interoperable AI Native RAN

arXiv:2507.18538v3 Announce Type: replace-cross Abstract: Artificial intelligence (AI) and machine learning (ML) are rapidly becoming integral to the 5G Radio Access Network (RAN), enabling beam manag

researcharxiv-cs-lg

Automatic weld seam segmentation for industrial quality control: a comparison of RGB and polarimetric imaging with CNN and transformer architectures

arXiv:2608.25465v1 Announce Type: new Abstract: Visual inspection of welded assemblies remains one of the least automated stages in many industrial production processes, still depending largely on the

safetyarxiv-cs-cv

Hardware

BanglaMamba: Exploring State Space Models for Bangla Fake News Detection

arXiv:2608.25190v1 Announce Type: new Abstract: Fake news detection has become an important Natural Language Processing (NLP) task due to the rapid spread of misinformation through online news platfor

hardwarearxiv-cs-cl

Beam Search, Self-Consistency, and the Limits of Inference-Time Scaling for Grammar-Constrained Text-to-SQL in Small Language Models

arXiv:2608.25761v1 Announce Type: new Abstract: One common trade-off in the use of large language models involves reducing the size of the model while increasing the amount of computation at inference

Behind the [MASK]: Disentangling Representation and Faithfulness in DAPF-Based Dementia Detection

arXiv:2608.25028v1 Announce Type: new Abstract: Spoken-language analysis via prompt-based domain-adaptive models is a promising direction for low-resource, non-invasive dementia screening, but such mo

CRAMER: Control via Request-Aware Masking for Editing Recommenders

arXiv:2608.25370v1 Announce Type: cross Abstract: Sequential recommendation models, while powerful, have limited flexibility in responding to immediate user requests, making it difficult to adapt thei

EgoNav: Bridging Learned Waypoints and Geometry-Aware Local Control for Robust Indoor Navigation

arXiv:2608.25642v1 Announce Type: new Abstract: Image-goal navigation using lightweight topological maps is a practical paradigm for indoor robot deployment: the map requires only geotagged images, an

safetyarxiv-cs-ro

Emergent Abilities in Large Language Models: A Survey

arXiv:2503.05788v3 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are leading a new technological revolution as one of the most promising research streams toward artificial genera

safetyarxiv-cs-cl

Hardware

ExFold: Unified Expert Folding for Training-Free MoE Prefill-Decode Acceleration

arXiv:2608.24938v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) models scale capacity for strong quality while keeping per-token compute bounded through sparse expert activation. Yet low-late

hardwarearxiv-cs-lg

FinRiskAtlas: Decision-Aligned Evaluation of Large Language Models for Financial Risk Review

arXiv:2608.25325v1 Announce Type: cross Abstract: Deploying large language models for professional financial review requires more than measuring general financial competence: models must perform the s

From Memorization to Absorption: Mixed-Policy RL for Continual Knowledge Injection

arXiv:2608.25243v1 Announce Type: new Abstract: Continual knowledge injection is essential for keeping large language models up-to-date in a fast-evolving world. Existing methods rely on supervised fi

safetyarxiv-cs-cl

Key Point Analysis Needs Structure Recovery: Task Definition, Dataset Diagnosis, and a Structure-Aware Benchmark

arXiv:2608.25854v1 Announce Type: new Abstract: Key Point Analysis (KPA) aims to identify a concise set of key points that summarize a collection of arguments together with their prevalence. We argue

Ladder Up, Memory Down: Low-Cost Fine-Tuning With Side Nets

arXiv:2512.14237v2 Announce Type: replace Abstract: Fine-tuning large language models (LLMs) is often limited by the memory available on commodity GPUs. Parameter-efficient fine-tuning (PEFT) methods

Applications

Layer-Order Inversion: Rethinking Latent Multi-Hop Reasoning in Large Language Models

arXiv:2601.03542v2 Announce Type: replace Abstract: Large language models (LLMs) perform well on multi-hop reasoning, yet how they internally compose multiple facts remains unclear. Recent work propos

applicationsarxiv-cs-cl