Search: “models”

Can LLMs Be Constrained to the Past? Improving Knowledge Cutoff through Recall-Based Prompting

arXiv:2606.05804v1 Announce Type: new Abstract: Prompted knowledge cutoff instructs a large language model (LLM) to act as if information beyond a specified cutoff date were unavailable. However, prio

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Compress-Distill: Reasoning Trace Compression for Efficient Knowledge Distillation

DGX agent

arXiv:2606.05988v1 Announce Type: cross Abstract: Reasoning models produce long chain-of-thought traces that are costly to distill and encourage verbose student outputs. We study post-hoc compression

model-releasesarxiv-cs-cl

5 Jun 2026

From Scoring to Explanations: Evaluating SHAP and LLM Rationales for Rubric-based Teaching Quality Assessment

arXiv:2606.05180v1 Announce Type: new Abstract: Automated scoring models are increasingly used to assign rubric-based quality ratings to complex language performances, including classroom transcripts,

researcharxiv-cs-cl

Harmonious Parameter Adaptation in Continual Visual Instruction Tuning for Safety-Aligned MLLMs

arXiv:2511.20158v2 Announce Type: replace Abstract: While continual visual instruction tuning (CVIT) has shown promise in adapting multimodal large language models (MLLMs), existing studies predominan

Improving Answer Extraction in Context-based Question Answering Systems Using LLMs

arXiv:2606.06197v1 Announce Type: new Abstract: Question answering (QA) systems have achieved notable progress with the advent of large language models (LLMs). However, they still face challenges in a

LLMs Can Leak Training Data But Do They Want To? A Propensity-Aware Evaluation of Memorization in LLMs

arXiv:2606.06286v1 Announce Type: new Abstract: Large language models can reproduce training data, but existing memorization evaluations mostly measure whether models can be forced to do so, rather th

researcharxiv-cs-cl

LongSpace: Exploring Long-Horizon Spatial Memory from Perception to Recall in Video

arXiv:2606.05677v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) have advanced image and video understanding and can increasingly handle longer visual inputs. Long-horizon ta

PEFT of SLM for Telecommunications Customer Support: A Comparative Study of LoRA Configurations with Energy Consumption Analysis

arXiv:2606.05176v1 Announce Type: new Abstract: While large language models (LLMs) show strong performance in natural language understanding and generation, their evaluation and adaptation to domain-s

ReasoningFlow: Discourse Structures for Understanding LLM Reasoning Traces

arXiv:2606.05402v1 Announce Type: new Abstract: Large reasoning models (LRMs) produce reasoning traces with non-linear structures, such as backtracking and self-correction, that complicate the evaluat

TensorBench: Benchmarking Coding Agents on a Compiler-Based Tensor Framework

arXiv:2606.05570v1 Announce Type: new Abstract: Repository-level coding benchmarks face a trade-off between task difficulty and evaluation reliability: tasks that challenge frontier models often invol

Thinking with Imagination: Agentic Visual Spatial Reasoning with World Simulators

arXiv:2606.06476v1 Announce Type: new Abstract: While Vision-Language Models (VLMs) have shown strong visual reasoning capabilities, their spatial reasoning abilities remain largely constrained to the

UltraVR: A Diagnostic Ultra-Resolution Image-VQA Benchmark for Evidence-Grounded Reasoning

arXiv:2606.05576v1 Announce Type: new Abstract: Vision-language models (VLMs) excel on visual question answering and multimodal reasoning benchmarks. Yet their capability on ultra-resolution images -

A Cookbook of 3D Vision: Data, Learning Paradigms, and Application

arXiv:2606.04291v1 Announce Type: new Abstract: 3D vision has rapidly evolved, driven by increasingly diverse data representations, learning paradigms, and modeling strategies. Yet the field remains f

Adaptive Minds: Empowering Agents with LoRA-as-Tools

arXiv:2510.15416v2 Announce Type: replace Abstract: We investigate a framework in which LoRA adapters are treated as callable tools that a base language model can dynamically select and invoke. We hyp

An Empirical Audit of Input Encoders for Multi-Channel Signal Transformers

arXiv:2606.04752v1 Announce Type: cross Abstract: Transformers consuming multi-channel scalar signals must embed C simultaneous values into one d_{ext{model}}-dimensional vector per time step. We empi

Bypassing Prompt Guards in Production with Controlled-Release Prompting

model-releasesarxiv-cs-lg

arXiv:2510.01529v3 Announce Type: replace Abstract: Ball et al. recently established that prompt filtering for AI alignment faces a fundamental barrier: under standard cryptographic assumptions, no fi

Can I Take Another Dose? Evaluating LLM Decision-Making Under Temporal Uncertainty in OTC Dosing QA

arXiv:2606.04262v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used for everyday health questions, including whether a user can safely take another dose of an over-the

Enhancing Hallucination Detection through Noise Injection

arXiv:2502.03799v4 Announce Type: replace Abstract: Large Language Models (LLMs) are prone to generating plausible yet incorrect responses, known as hallucinations. Effectively detecting hallucination

researcharxiv-cs-cl

Agents

FALSIFYBENCH: Evaluating Inductive Reasoning in LLMs with Rule Discovery Games

arXiv:2606.04751v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed as autonomous agents in scientific tasks. Yet whether these systems can effectively engage in for

agentsarxiv-cs-ai

Safety

Few Tokens, Big Leverage: Preserving Safety Alignment by Constraining Safety Tokens during Fine-tuning

arXiv:2603.07445v2 Announce Type: replace Abstract: Large language models (LLMs) often require fine-tuning (FT) to perform well on downstream tasks, but FT can induce safety-alignment drift even when

safetyarxiv-cs-cl

Knowledge Index of Noah's Ark

arXiv:2606.05104v1 Announce Type: new Abstract: Knowledge benchmarks for LLMs face three issues: scaling-driven designs that do not operationalize disciplinary representativeness; flat-payment annotat

LLMs + Persona-Plug = Personalized LLMs

arXiv:2409.11901v2 Announce Type: replace Abstract: Personalization plays a critical role in numerous language tasks and applications, since users with the same requirements may prefer diverse outputs

Making Expert Reasoning Learnable with Self-Distillation

arXiv:2602.02405v2 Announce Type: replace-cross Abstract: Improving the reasoning capabilities of large language models (LLMs) typically relies either on the model's ability to sample a correct soluti

Multi-SPIN: Multi-Access Speculative Inference for Cooperative Token Generation at the Edge

arXiv:2606.04581v1 Announce Type: cross Abstract: Speculative inference (SPIN) was originally developed as an efficient architecture to accelerate Large Language Models (LLMs). In this work, we propos

SAM 3D: 3Dfy Anything in Images

arXiv:2511.16624v2 Announce Type: replace-cross Abstract: We present SAM 3D, a generative model for visually grounded 3D object reconstruction, predicting geometry, texture, and layout from a single i

Spectral Scaling Laws of Muon

arXiv:2606.04058v1 Announce Type: cross Abstract: Orthonormalized update rules have rapidly become a leading choice of optimizer for training large language models, with recent open-source state-of-th

VAMPS: Visual-Assisted Mathematical Problem Solving Benchmark

arXiv:2606.04244v1 Announce Type: new Abstract: Multimodal large language models are increasingly capable of complex reasoning, yet their performance often degrades when they must externalize a proble

VCIFBench: Evaluating Complex Instruction Following for Video Understanding

arXiv:2606.04588v1 Announce Type: new Abstract: Multimodal large language models have made rapid progress in video understanding, yet existing benchmarks largely rely on simple prompts and provide lim

AI Rater Discrimination Depends on Scoring Protocol in Complex Clinical Decision-Making

arXiv:2606.03198v1 Announce Type: cross Abstract: Clinical AI evaluation increasingly delegates scoring to large language models (LLMs) acting as AI raters, yet their scoring behavior across evaluatio

An Asymptotic Theory of Chain-of-Thought in In-Context Learning

model-releasesarxiv-cs-lg

arXiv:2606.03217v1 Announce Type: cross Abstract: Chain-of-thought (CoT) reasoning has become a widely used mechanism for eliciting multi-step reasoning in large language models by generating intermed

Anomalies in Multivariate Time Series Benchmarks Are Mostly Univariate

arXiv:2606.02670v1 Announce Type: cross Abstract: Many recent multivariate time series anomaly detection (MT-SAD) models incorporate cross-channel modeling, under the implicit assumption that the stru

ATLAS: A Large-Scale Evaluation Benchmark for Adversarial LiDAR Perception

arXiv:2606.02924v1 Announce Type: new Abstract: Autonomous driving perception is typically evaluated on clean benchmark data, yet real-world deployment requires robustness to rare, structured, and pot

AUDITFLOW: Executable Symbolic Environments for Structured Financial Reporting Verification

arXiv:2606.03031v1 Announce Type: new Abstract: Structured financial audit verification is difficult for language-model agents because correctness depends on structured evidence rather than text alone

Beyond Encoder Accumulation: Measuring Encoder Roles in Multi-Encoder VLMs

arXiv:2606.03879v1 Announce Type: cross Abstract: As foundation models scale toward fusing more heterogeneous visual streams, understanding how diverse encoders interact under joint training becomes a

Causal Neural Probabilistic Circuits

arXiv:2603.01372v2 Announce Type: replace-cross Abstract: Concept Bottleneck Models (CBMs) enhance the interpretability of end-to-end neural networks by introducing a layer of concepts and predicting

DECA: Decentralizing Block-Wise Adam for Efficient LLM Full-Parameter Fine-Tuning on Non-IID Data

model-releasesarxiv-cs-lg

arXiv:2606.03209v1 Announce Type: new Abstract: Fine-tuning large language models (LLMs) in privacy-sensitive and resource-constrained environments remains challenging. Since training data are often d

DiffUNet^2: Bidirectional Prediction, Probabilistic Generation and Collaborative Visual Discovery for Scientific Data

arXiv:2606.03926v1 Announce Type: cross Abstract: Modeling temporal evolution is important to analyzing and reasoning about scientific phenomena, yet most machine learning methods provide deterministi

researcharxiv-cs-lg

Safety

Effect of Demographic Bias on Skin Lesion Classification

arXiv:2606.03214v1 Announce Type: new Abstract: In this study, we evaluate the performance of skin lesion classification using ResNet-based convolutional models, focusing on the impact of demographic

safetyarxiv-cs-ai

From Script to Semantics: Prompting Strategies for African NLI

arXiv:2606.03304v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly evaluated in multilingual settings, yet their inference behavior in low-resource African languages remains

GTBench: A Curriculum-Grounded Benchmark for Evaluating LLMs as Mathematical Research Assistants in Graph Theory

arXiv:2606.03144v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used as self-study assistants in technical disciplines, yet their reliability as mathematical reasoning as

InftyThink+: Effective and Efficient Infinite-Horizon Reasoning via Reinforcement Learning

arXiv:2602.06960v3 Announce Type: replace-cross Abstract: Large reasoning models achieve strong performance by scaling inference-time chain-of-thought, but this paradigm suffers from quadratic cost, c

Let the Dynamics Flow: Stable Flow Matching Dynamical Systems

model-releasesarxiv-cs-ro

arXiv:2606.03834v1 Announce Type: new Abstract: Flow matching has recently emerged as a powerful approach for imitation learning, enabling scalable, expressive, and multimodal motion policies. However

Multilingual Unlearning in LLMs: Transfer, Dynamics, and Reversibility

arXiv:2606.03291v1 Announce Type: new Abstract: Large language models (LLMs) can memorize sensitive facts, motivating unlearning methods that remove targeted knowledge without costly retraining. Howev

Agents

MUSE: A Unified Agentic Harness for MLLMs

arXiv:2606.03005v1 Announce Type: cross Abstract: Despite rapid progress, multimodal large language models (MLLMs) still fail on tasks that humans solve effortlessly, such as navigating a grid maze fr

agentsarxiv-cs-ai

SCOPE: Real-Time Natural Language Camera Agent at the Edge

arXiv:2606.02951v1 Announce Type: cross Abstract: Deploying language-driven agents in robotics requires evaluations that reflect real-world task demands: natural-language instructions with reproducibl

The Reliability Gap in Benchmark Auditing: Distribution Shift and Scale as Failure Modes of Contamination Detection

arXiv:2606.03305v1 Announce Type: new Abstract: Benchmark contamination, where evaluation examples appear in a model's training data, threatens the validity of LLM assessment. Statistical tools for de

A Direct Approach for Handling Contextual Bandits with Latent State Dynamics

arXiv:2604.08149v2 Announce Type: replace Abstract: We consider a linear contextual bandit model where contexts and rewards are governed by a finite hidden Markov chain. We first revisit the simplifie

researcharxiv-cs-lg

2 Jun 2026

A Local Perturbation Theory for Cross-Domain Interference and Recovery in Multi-Domain RL

arXiv:2606.02398v1 Announce Type: cross Abstract: Reinforcement learning (RL) post-training improves large language models (LLMs) on individual domains such as mathematical reasoning, code generation,