Search: “models”

arXiv:2504.17052v4 Announce Type: replace Abstract: Existing evaluations of political bias in large language models (LLMs) typically classify outputs as left- or right-leaning. We extend this perspect

safetyarxiv-cs-cl

28 Jul 2026

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Risk Governance for Generative AI Mental Health Support: A Multi-Turn Safety Architecture

DGX agent

arXiv:2607.22692v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used for emotional support despite lacking mechanisms to safely govern evolving mental health risk. Existi

model-releasesarxiv-cs-ai

28 Jul 2026

TRIDENT: Benchmarking LLM Safety in Finance, Medicine, and Law

arXiv:2507.21134v2 Announce Type: replace Abstract: As large language models (LLMs) are increasingly deployed in high-risk domains such as law, finance, and medicine, systematically evaluating their d

28 Jul 2026

TriShieldRAG: A Three-Ring Defense-in-Depth Framework Against Knowledge Corruption in Retrieval-Augmented Generation

arXiv:2607.23838v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) lets a large language model answer questions using documents retrieved from an external knowledge base at query t

28 Jul 2026

The Hard Decision Layer: Evidence for Committed Inference in Transformers

arXiv:2607.21613v1 Announce Type: cross Abstract: We investigate where and how transformer-based language models commit to predictions in multiple-choice question answering. We identify the _Hard Deci

27 Jul 2026

WHBench: Evaluating Frontier LLMs with Expert-in-the-Loop Validation on Women's Health Topics

arXiv:2604.00024v2 Announce Type: replace Abstract: Large language models are increasingly used for medical guidance, but women's health remains under-evaluated in benchmark design. We present the Wom

27 Jul 2026

A Knowledge-Injection Framework for Zero-Shot Adaptation of LLMs to Delirium Prediction

arXiv:2607.20453v1 Announce Type: cross Abstract: Large language models show promise for clinical prediction, but zero-shot performance on specialized tasks is limited by incomplete domain knowledge,

24 Jul 2026

It's Not What You Say, It's How You Say It: Evaluating LLM Responses to Expressions of Belief

arXiv:2607.18232v2 Announce Type: replace Abstract: Users frequently express their beliefs to large language models (LLMs). In some situations, the LLM should accept these contextual beliefs as true.

23 Jul 2026

Adaptive Testing for LLM Evaluation: A Psychometric Alternative to Static Benchmarks

arXiv:2511.04689v3 Announce Type: replace-cross Abstract: Evaluating large language models (LLMs) typically requires thousands of benchmark items, making the process expensive, slow, and increasingly

MobileSAM2: Lightweight Segment Anything for Spatial Intelligence

arXiv:2607.12297v1 Announce Type: new Abstract: The recent large video foundation model, SAM2, enables segment anything in both images and videos, serving as a powerful base model for various applicat

researcharxiv-cs-cv

Self in Space: Benchmarking Self-Awareness and Spatial Cognition in UAV Embodied Intelligence

arXiv:2607.12477v1 Announce Type: new Abstract: Autonomous UAV systems increasingly rely on multimodal large language models (MLLMs) to operate in complex real-world environments. Such embodied scenar

The Illusion of Robustness: Aggregate Accuracy Hides Prediction Flips under Task-Irrelevant Context

arXiv:2607.12963v1 Announce Type: new Abstract: As large language models (LLMs) grow more capable, they are increasingly deployed in context-rich settings where task inputs are often accompanied by lo

LUMI: Tokenizer-Agnostic LLM-Based Lossless Image Compression

arXiv:2607.08221v1 Announce Type: new Abstract: Large language model (LLM)-based lossless image compression methods typically represent pixel data through the native text interface of a pretrained mod

10 Jul 2026

Validity of LLMs as data annotators: AMALIA on authority

arXiv:2607.08731v1 Announce Type: cross Abstract: A national language model offers a linguistic community its own instrument for measuring what its citizens say and value. Portugal's AMALIA, a publicl

10 Jul 2026

Can Reinforcement Learning Efficiently Discover Price Manipulation?

arXiv:2607.06121v1 Announce Type: cross Abstract: In this paper, we investigate whether a model-free RL agent can identify and exploit price manipulation opportunities more effectively than a traditio

9 Jul 2026

Safety

Framing Instability in LLM Ethical Stance: Auditing Negation Sensitivity in Moral Dilemmas

arXiv:2601.21433v2 Announce Type: replace Abstract: Language models are increasingly consulted on ethically consequential questions, yet the stance a model expresses may not survive a change in framin

safetyarxiv-cs-ai

9 Jul 2026

Predicting LLM Safety Before Release by Simulating Deployment

arXiv:2607.07184v1 Announce Type: cross Abstract: Pre-deployment safety evaluations aim to inform the downstream risks of releasing a new AI model. Yet most evaluations provide limited evidence about

9 Jul 2026

Enhancement of E-commerce Sponsored Search Relevancy with LLM

arXiv:2607.03886v1 Announce Type: cross Abstract: Sponsored search plays a crucial role as a revenue stream for search engines, wherein advertisers competitively bid on keywords that align with the us

ICR-RL: Deep Reinforcement Learning via In-Context Regression

arXiv:2509.11259v2 Announce Type: replace-cross Abstract: Recent advancements in machine learning have largely been driven by foundation models (FMs) trained on large, diverse datasets, enabling them

NEST: Nascent Encoded Steganographic Thoughts

arXiv:2602.14095v2 Announce Type: replace Abstract: Monitoring chain-of-thought (CoT) reasoning is a foundational safety technique for large language model agents; however, this oversight is compromis

OrthoReg: Orthogonal Regularization for Hybrid Symbolic-Neural Dynamical Systems

arXiv:2606.19145v2 Announce Type: replace-cross Abstract: Dynamical systems are fundamental to modeling the natural world, yet modeling them involves a persistent trade-off: manually prescribed mechan

Ask the Right Comparison:Bias-Aware Bayesian Active Top-k Ranking with LLM Judges

arXiv:2607.02104v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used as cheap, scalable judges that compare candidate outputs pairwise -- to rank responses, select models

3 Jul 2026

Neural Network-Based Estimation of Time-Dependent Parameters in AR(p) Processes

arXiv:2607.00470v1 Announce Type: cross Abstract: We investigate a forecasting framework based on a simple discrete-time dynamic model with coefficients varying in time. The parameters of the model ar

2 Jul 2026

From Failure Taxonomy to Intervention: A Diagnostic Methodology for Industry-Scale AVLM in Video and Live-Streaming Platform Moderation

arXiv:2606.30059v1 Announce Type: new Abstract: Industry-scale video and live-streaming moderation imposes requirements that are difficult to satisfy with generic pretrained public models or external

30 Jun 2026

Health-ORSC-Bench: A Benchmark for Measuring Over-Refusal and Safety Completion in Health Context

arXiv:2601.17642v2 Announce Type: replace Abstract: Safety alignment in Large Language Models is critical for healthcare; however, reliance on binary refusal boundaries often results in over-refusal o

29 Jun 2026

Training-free Truthfulness Detection via Sparse MLP Value Vectors

arXiv:2509.17932v2 Announce Type: replace Abstract: Large language models (LLMs) are prone to generating factually incorrect content, motivating methods for assessing truthfulness from internal model

29 Jun 2026

Closing the Quality Gap in Low-Resource Text-to-Speech: LoRA Fine-Tuning of VoxCPM2 for Khmer and Korean

arXiv:2606.26618v1 Announce Type: new Abstract: Large pretrained text-to-speech (TTS) models sound almost human for well-resourced languages, but much worse for languages that are rare in their traini

26 Jun 2026

SocialPersona: Benchmarking Personalized Profiling and Response with Multimodal Social-Media Context

arXiv:2606.26654v1 Announce Type: new Abstract: Personalized language-model assistants are often evaluated through a memory lens: can a model recall preferences users have explicitly stated in dialogu

26 Jun 2026

Reinforcement Learning Improves Traversal of Parametric Knowledge in LLMs

arXiv:2511.05933v2 Announce Type: replace Abstract: Reinforcement learning (RL) is often credited with improving language model reasoning at the expense of knowledge. We challenge this narrative by sh

researcharxiv-cs-cl

25 Jun 2026

Not All Invariants Are Equal: Curating Training Data to Accelerate Program Verification with SLMs

arXiv:2603.15510v2 Announce Type: replace Abstract: The synthesis of inductive loop invariants remains a critical bottleneck in automated program verification. While Large Language Models (LLMs) show

24 Jun 2026

You Don't Need to Run Every Eval

arXiv:2606.24020v1 Announce Type: new Abstract: A modern model release reports scores on 40+ benchmarks and the same evaluations were run many more times before it: to track training progress, compare

24 Jun 2026

Each Judge Its Own Yardstick: Discovering Per-VLM Taxonomies for Physical Video Evaluation

arXiv:2606.22918v1 Announce Type: new Abstract: Maintaining physical consistency in video generators and world models increasingly relies on vision-language models (VLMs) as automated judges that prov

Essential Subspace Merging for Multi-Task Learning

arXiv:2606.19164v2 Announce Type: replace Abstract: Model merging aims to enable multi-task learning by integrating the capabilities of multiple models fine-tuned from the same pre-trained checkpoint

Hybrid Compression: Integrating Pruning and Quantization for Optimized Neural Networks

arXiv:2606.22935v1 Announce Type: new Abstract: Deep neural networks have witnessed remarkable advancements in recent years and have become integral to various applications. However, alongside these d

Physiology-Aware CNN and Zero-Shot Multimodal LLMs for ECG Image Classification: A Comparative Study

arXiv:2606.22889v1 Announce Type: new Abstract: Multimodal large language models (LLMs) are increasingly adopted to interpret 12-lead ECG images, though the interpretations often lack validation. Howe

How Seemingly Inconsequential Design Choices Dictate Performance of LLMs in Pathology

arXiv:2606.12407v1 Announce Type: new Abstract: General-purpose large language models (LLMs) are routinely used as baselines when evaluating specialized pathology models on whole-slide images (WSIs).

11 Jun 2026

Alignment Collapse Under KV Cache Quantization: Diagnosis and Mitigation

arXiv:2606.09864v1 Announce Type: cross Abstract: Key-value (KV) cache quantization is widely used to reduce Large Language Model (LLM) inference memory, yet existing evaluations solely focus on measu

10 Jun 2026

Applications

A Mechanistic Analysis of Adversarial Fine-tuning of Vision Transformers

arXiv:2606.07593v1 Announce Type: cross Abstract: The widespread use of image classification models in high-risk, real-world situations necessitates making these models robust to slight disturbances o

applicationsarxiv-cs-ai

9 Jun 2026

Correcting Mean Bias in Text Embeddings: A Refined Renormalization with Training-Free Improvements on MMTEB

arXiv:2511.11041v2 Announce Type: replace-cross Abstract: We find that current sentence-embedding models produce outputs with a consistent bias: every embedding e decomposes as ilde e + mu, where the

9 Jun 2026

UnpredictaBench: A Benchmark for Evaluating Distributional Randomness in LLMs

arXiv:2606.06622v1 Announce Type: new Abstract: We introduce UnpredictaBench, an evaluation that tests the ability of large language models (LLMs) to capture true underlying distributions. As LLMs are

8 Jun 2026

TLA-Prover: Verifiable TLA+ Specification Synthesis via Preference-Optimized Low-Rank Adaptation

arXiv:2606.06133v1 Announce Type: cross Abstract: TLA+ is a formal specification language for verifying distributed systems and safety-critical protocols. Large language models (LLMs) frequently produ

6 Jun 2026

Predicting Inference-Time Scaling Gains from Labeled Validation-Set Output Statistics

arXiv:2606.02981v1 Announce Type: new Abstract: Best-of-N inference scaling (drawing N candidate answers from a language model and returning the one a reward model ranks highest) improves accuracy by

researcharxiv-cs-cl

3 Jun 2026

Safety Measurements for Fine-tuned LLMs Should be Grounded in Capability

arXiv:2606.03648v1 Announce Type: cross Abstract: Adapting foundation large language models to a user's task or preferred style through fine-tuning can result in compromising the model's safety. Previ

3 Jun 2026

SenseJudge: Human-Centric Preference-Driven Judgment Framework

arXiv:2606.03189v1 Announce Type: new Abstract: Large Language Models (LLMs) as judges across various scenarios such as assessing model responses is becoming an increasingly accepted paradigm. However

3 Jun 2026

Does Compression Preserve Uncertainty? A Unified Benchmark for Quantized and Sparse LLMs via Conformal Prediction

arXiv:2606.01850v1 Announce Type: new Abstract: Model compression techniques such as quantization and pruning are widely used to reduce the deployment cost of large language models (LLMs), with existi

Pause and Think: A Dataset and Benchmark for Video-Grounded Assistive Action Suggestion

arXiv:2606.00616v1 Announce Type: cross Abstract: Recent Vision-Language Models (VLMs) struggle with grounded reasoning, temporal consistency, and context aware planning in videos. We introduce pause-

Physics-Guided Recurrent State-Space Neural Networks for Multi-Step Prediction

arXiv:2606.02278v1 Announce Type: cross Abstract: State-space models are traditionally based on physical knowledge, but multi-step predictions from these physical models can be poor due to model inacc

researcharxiv-cs-lg

StemBind: When MLLMs Get Lost Between Rules and Instances in Abstract Visual Reasoning

arXiv:2606.00148v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) often know the rule but pick the wrong answer: on abstract visual reasoning (AVR) tasks, a model can describe