Search: “llm”

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

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

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

DGX agent

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

model-releasesarxiv-cs-ai

28 Aug 2026

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

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

Heat!

model-releasesr-localllama

As in actual physical heat. How are people coping with the heat that running a decent inference rig pumps out? I've got dual 5060 Ti GPUs and a relatively modest CPU (Intel 14 Core Ultra 5 245KF Deskt

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.,

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

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

Spreading the load: How Salesforce met Multi-AZ HA with SageMaker Inference Components

model-releasesaws-ml-blog

Learn how Salesforce used Amazon SageMaker AI Inference Component placement (the SchedulingConfig parameter) to distribute model copies across multiple Availability Zones, meeting their Multi-AZ high

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