Search: “models”

IntentGrasp: A Comprehensive Benchmark for Intent Understanding

arXiv:2605.06832v1 Announce Type: cross Abstract: Accurately understanding the intent behind speech, conversation, and writing is crucial to the development of helpful Large Language Model (LLM) assis

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Large Video Planner Enables Generalizable Robot Control

DGX agent

arXiv:2512.15840v2 Announce Type: replace-cross Abstract: General-purpose robots require decision-making models that generalize across diverse tasks and environments. Recent works build robot foundati

applicationsarxiv-cs-cv

11 May 2026

MathlibPR: Pull Request Merge-Readiness Benchmark for Formal Mathematical Libraries

arXiv:2605.07147v1 Announce Type: cross Abstract: The ecosystem of Lean and Mathlib has become the de facto standard for large language model (LLM) assisted formal reasoning with remarkable successes

MAVEN: Multi-Agent Verification-Elaboration Network with In-Step Epistemic Auditing

arXiv:2605.07646v1 Announce Type: cross Abstract: While explicit reasoning trajectories enhance model interpretability, existing paradigms often rely on monolithic chains that lack intermediate verifi

Neural Neural Scaling Laws

arXiv:2601.19831v2 Announce Type: replace-cross Abstract: Neural scaling laws predict how language model performance improves with increased training inputs. While aggregate metrics like validation lo

Rep2Text: Decoding Full Text from a Single LLM Token Representation

arXiv:2511.06571v3 Announce Type: replace-cross Abstract: Large language models (LLMs) have achieved remarkable progress across diverse tasks, yet their internal mechanisms remain largely opaque. In t

Rethinking Experience Utilization in Self-Evolving Language Model Agents

arXiv:2605.07164v1 Announce Type: new Abstract: Self-evolving agents improve by accumulating and reusing experience from past interactions. Existing work has largely focused on how experience is const

researcharxiv-cs-cl

SR^2-LoRA: Self-Rectifying Inter-layer Relations in Low-Rank Adaptation for Class-Incremental Learning

arXiv:2605.07420v1 Announce Type: cross Abstract: Pre-trained models with parameter-efficient fine-tuning (PEFT) have demonstrated promising potential for class-incremental learning (CIL), yet catastr

Unsolvability Ceiling in Multi-LLM Routing: An Empirical Study of Evaluation Artifacts

arXiv:2605.07395v1 Announce Type: cross Abstract: Efficient routing across multiple LLMs enables cost-quality tradeoffs by directing queries to the cheapest capable model. Prior work attributes routin

Are LLMs Ready for Conflict Monitoring? Empirical Evidence from West Africa

arXiv:2605.04177v1 Announce Type: new Abstract: As LLMs enter conflict monitoring, understanding systematic distortions in their outputs is critical for humanitarian accountability. We evaluate four v

From Parameter Dynamics to Risk Scoring : Quantifying Sample-Level Safety Degradation in LLM Fine-tuning

arXiv:2605.04572v1 Announce Type: cross Abstract: Safety alignment of Large Language Models (LLMs) is extremely fragile, as fine-tuning on a small number of benign samples can erase safety behaviors l

Frontier Lag: A Bibliometric Audit of Capability Misrepresentation in Academic AI Evaluation

arXiv:2605.04135v1 Announce Type: cross Abstract: Readers of applied-domain LLM capability evaluations want to know what AI systems can currently do. That literature answers a related, but consequenti

Safety

High-Fidelity Single-Image Head Modeling with Industry-Grade Topology

arXiv:2605.04524v1 Announce Type: new Abstract: We present a single-image head mesh reconstruction framework that addresses the longstanding challenge of simultaneously preserving facial identity and

safetyarxiv-cs-cv

Laundering AI Authority with Adversarial Examples

arXiv:2605.04261v1 Announce Type: cross Abstract: Vision-language models (VLMs) are increasingly deployed as trusted authorities -- fact-checking images on social media, comparing products, and modera

Memory as a Markov Matrix: Sample Efficient Knowledge Expansion via Token-to-Dictionary Mapping

arXiv:2605.04308v1 Announce Type: new Abstract: Continual incorporation of new knowledge is essential for the long-term evolution of large language models (LLMs). Existing approaches typically rely on

Paraphrase-Induced Output-Mode Collapse: When LLMs Break Character Under Semantically Equivalent Inputs

arXiv:2605.04665v1 Announce Type: new Abstract: When the substantive content of a request is rewritten, do large language models still answer in the format the original task asked for? We find that th

Are LLMs More Skeptical of Entertainment News?

arXiv:2605.01727v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used for automated news credibility assessment, yet it remains unclear whether they apply even-handed stan

Atomic Fact-Checking Increases Clinician Trust in Large Language Model Recommendations for Oncology Decision Support: A Randomized Controlled Trial

arXiv:2605.03916v1 Announce Type: new Abstract: Question: Does atomic fact-checking, which decomposes AI treatment recommendations into individually verifiable claims linked to source guideline docume

researcharxiv-cs-cl

Exposing LLM Safety Gaps Through Mathematical Encoding:New Attacks and Systematic Analysis

arXiv:2605.03441v1 Announce Type: cross Abstract: Large language models (LLMs) employ safety mechanisms to prevent harmful outputs, yet these defenses primarily rely on semantic pattern matching. We s

Geometric Deviation as an Unsupervised Pre-Generation Reliability Signal: Probing LLM Representations for Answerability

arXiv:2605.03196v1 Announce Type: new Abstract: A reliable language model should be able to signal, prior to generation, when a query falls outside its knowledge. We investigate whether representation

Maximizing mutual information between prompts and responses improve LLM personalization with no additional data or human oversight

arXiv:2603.19294v2 Announce Type: replace-cross Abstract: While post-training has successfully improved large language models (LLMs) across a variety of domains, these gains heavily rely on human-labe

Parameter-Efficient Distributional RL via Normalizing Flows and a Geometry-Aware Cramer Surrogate

arXiv:2505.04310v2 Announce Type: replace-cross Abstract: Distributional Reinforcement Learning (DistRL) improves upon expectation-based methods by modeling full return distributions, but standard app

Pose Tracking with a Foundation Pose Model and an Ensemble Directional Kalman Filter

arXiv:2605.03105v1 Announce Type: new Abstract: This paper introduces the ensemble directional Kalman filter (EnDKF), an ensemble-based Kalman filtering approach for pose tracking that jointly estimat

researcharxiv-cs-lg

Reward Hacking Benchmark: Measuring Exploits in LLM Agents with Tool Use

arXiv:2605.02964v1 Announce Type: new Abstract: Reinforcement learning (RL) trained language model agents with tool access are increasingly deployed in coding assistants, research tools, and autonomou

Self-Mined Hardness for Safety Fine-Tuning

arXiv:2605.03226v1 Announce Type: new Abstract: Safety fine-tuning of language models typically requires a curated adversarial dataset. We take a different approach: score each candidate prompt's diff

The Right Answer, the Wrong Direction: Why Transformers Fail at Counting and How to Fix It

arXiv:2605.03258v1 Announce Type: cross Abstract: Large language models often fail at simple counting tasks, even when the items to count are explicitly present in the prompt. We investigate whether t

When Prompts Interact: Assessing Prompt Arithmetic for Deconfounding under Distribution Shift

arXiv:2605.03096v1 Announce Type: cross Abstract: In classification tasks, models may rely on confounding variables to achieve strong in-distribution performance, capturing spurious features that fail

A Light Weight Multi-Features-View Convolution Neural Network For Plant Disease Identification

arXiv:2605.00903v1 Announce Type: new Abstract: Agriculture is a key sector of the economies of developing countries. It serves as a primary source of income and employment for rural populations. Howe

Adaptive Texture-aware Masking for Self-Supervised Learning in 3D Dental CBCT Analysis

arXiv:2605.01741v1 Announce Type: new Abstract: Cone Beam Computed Tomography (CBCT) is pivotal for 3D diagnostic imaging in dentistry. However, the development of robust AI models for volumetric anal

Anon: Extrapolating Optimizer Adaptivity Across the Real Spectrum

arXiv:2605.02317v1 Announce Type: cross Abstract: Adaptive optimizers such as Adam have achieved great success in training large-scale models like large language models and diffusion models. However,

researcharxiv-cs-lg

ARIS: Agentic and Relationship Intelligence System for Social Robots

model-releasesarxiv-cs-ro

arXiv:2605.00943v1 Announce Type: new Abstract: Foundational models have advanced social robotics, enabling richer perception and communicative interaction with users. However, current systems still s

Compute Optimal Tokenization

arXiv:2605.01188v1 Announce Type: new Abstract: Scaling laws enable the optimal selection of data amount and language model size, yet the impact of the data unit, the token, on this relationship remai

Embedding-based In-Context Prompt Training for Enhancing LLMs as Text Encoders

arXiv:2605.01372v1 Announce Type: new Abstract: Large language models (LLMs) have been widely explored for embedding generation. While recent studies show that in-context learning (ICL) effectively en

Safety

Momentum-Anchored Multi-Scale Fusion Model for Long-Tailed Chest X-Ray Classification

arXiv:2605.02292v1 Announce Type: new Abstract: Chest X-ray classification suffers from severe class imbalance where gradient updates bias toward majority classes, causing feature drift and poor perfo

safetyarxiv-cs-cv

Multi-fidelity surrogates for mechanics of composites: from co-kriging to multi-fidelity neural networks

arXiv:2605.02871v1 Announce Type: cross Abstract: Composite materials exhibit strongly hierarchical and anisotropic properties governed by coupled mechanisms spanning constituents, plies, laminates, s

Applications

Multimodal Confidence Modeling in Audio-Visual Quality Assessment

arXiv:2605.01219v1 Announce Type: cross Abstract: Audio-visual quality assessment (AVQA) is essential for streaming, teleconferencing, and immersive media. In realistic streaming scenarios, distortion

applicationsarxiv-cs-cv

On Stable Long-Form Generation: Benchmarking and Mitigating Length Volatility

arXiv:2605.01357v1 Announce Type: new Abstract: Large Language Models (LLMs) excel at long-context understanding but exhibit significant limitations in long-form generation. Existing studies primarily

PepSpecBench: A Unified Evaluation Benchmark for Peptide Tandem Mass Spectrometry Prediction

arXiv:2605.01945v1 Announce Type: new Abstract: Tandem mass spectrometry provides a high-throughput framework for identifying and quantifying proteins in complex biological samples. In computational p

Robust Parameter Learning for Uncertain MDPs

arXiv:2605.01339v1 Announce Type: new Abstract: Learning-based approaches to verifying unknown Markov decision processes (MDPs) often employ uncertain MDPs. These models use, for example, confidence i

SF20K Competition 2025: Summary and findings

arXiv:2605.01496v1 Announce Type: new Abstract: This report presents the results and findings of the first edition of the Short-Films 20K (SF20K) Competition, held in conjunction with the SLoMO Worksh

Standing on the Shoulders of Giants: Stabilized Knowledge Distillation for Cross--Language Code Clone Detection

arXiv:2605.02860v1 Announce Type: cross Abstract: Cross-language code clone detection (X-CCD) is challenging because semantically equivalent programs written in different languages often share little

The Compliance Trap: How Structural Constraints Degrade Frontier AI Metacognition Under Adversarial Pressure

arXiv:2605.02398v1 Announce Type: cross Abstract: As frontier AI models are deployed in high-stakes decision pipelines, their ability to maintain metacognitive stability -- knowing what they do not kn

Safety

The Model Knows, the Decoder Finds: Future Value Guided Particle Power Sampling

arXiv:2605.02427v1 Announce Type: cross Abstract: A recurring pattern in 'reasoning without training' is that base LLMs already assign non-trivial probability mass to correct multi-step solutions; the

safetyarxiv-cs-lg

Understanding the Performance Plateau in Text-to-Video Retrieval: A Comprehensive Empirical and Linguistic Analysis

arXiv:2605.00826v1 Announce Type: cross Abstract: Text-to-video retrieval enables users to find relevant video content using natural language queries, a task that has grown increasingly important with

Alethia: A Foundational Encoder for Voice Deepfakes

arXiv:2605.00251v1 Announce Type: cross Abstract: Existing voice deepfake detection and localization models rely heavily on representations extracted from speech foundation models (SFMs). However, dow

Caracal: Causal Architecture via Spectral Mixing

arXiv:2605.00292v1 Announce Type: new Abstract: The scalability of Large Language Models to long sequences is hindered by the quadratic cost of attention and the limitations of positional encodings. T

Evaluating the Architectural Reasoning Capabilities of LLM Provers via the Obfuscated Natural Number Game

arXiv:2605.00677v1 Announce Type: new Abstract: While Large Language Models have achieved notable success on formal mathematics benchmarks such as MiniF2F, it remains unclear whether these results ste

From Prediction to Practice: A Task-Aware Evaluation Framework for Blood Glucose Forecasting

arXiv:2605.00645v1 Announce Type: new Abstract: Clinical time-series forecasting is increasingly studied for decision support, yet standard aggregate metrics can obscure whether a model is actually us