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Entropy Ratio Clipping as a Soft Global Constraint for Stable Reinforcement Learning

arXiv:2512.05591v2 Announce Type: replace-cross Abstract: Large language model post-training relies on reinforcement learning to improve model capability and alignment quality. However, the off-policy

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Equity Bias: An Ethical Framework for AI Design

DGX agent

arXiv:2604.21907v1 Announce Type: cross Abstract: Equity Bias is a philosophical and practical framework for building smarter, more equitable AI systems. Grounded in hermeneutic philosophy and epistem

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24 Apr 2026

ERA: Evidence-based Reliability Alignment for Honest Retrieval-Augmented Generation

arXiv:2604.20854v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) grounds language models in factual evidence but introduces critical challenges regarding knowledge conflicts betw

Escaping the Agreement Trap: Defensibility Signals for Evaluating Rule-Governed AI

arXiv:2604.20972v1 Announce Type: new Abstract: Content moderation systems are typically evaluated by measuring agreement with human labels. In rule-governed environments this assumption fails: multip

Fairness Evaluation and Inference Level Mitigation in LLMs

arXiv:2510.18914v4 Announce Type: replace-cross Abstract: Large language models often display undesirable behaviors embedded in their internal representations, undermining fairness, inconsistency drif

Fairness under uncertainty in sequential decisions

arXiv:2604.21711v1 Announce Type: cross Abstract: Fair machine learning (ML) methods help identify and mitigate the risk that algorithms encode or automate social injustices. Algorithmic approaches al

FairQE: Multi-Agent Framework for Mitigating Gender Bias in Translation Quality Estimation

arXiv:2604.21420v1 Announce Type: new Abstract: Quality Estimation (QE) aims to assess machine translation quality without reference translations, but recent studies have shown that existing QE models

Fine-Grained Perspectives: Modeling Explanations with Annotator-Specific Rationales

arXiv:2604.21667v1 Announce Type: cross Abstract: Beyond exploring disaggregated labels for modeling perspectives, annotator rationales provide fine-grained signals of individual perspectives. In this

FingerViP: Learning Real-World Dexterous Manipulation with Fingertip Visual Perception

arXiv:2604.21331v1 Announce Type: new Abstract: The current practice of dexterous manipulation generally relies on a single wrist-mounted view, which is often occluded and limits performance on tasks

Flipping Against All Odds: Reducing LLM Coin Flip Bias via Verbalized Rejection Sampling

arXiv:2506.09998v2 Announce Type: replace-cross Abstract: Large language models (LLMs) can often accurately describe probability distributions using natural language, yet they still struggle to genera

From If-Statements to ML Pipelines: Revisiting Bias in Code-Generation

arXiv:2604.21716v1 Announce Type: new Abstract: Prior work evaluates code generation bias primarily through simple conditional statements, which represent only a narrow slice of real-world programming

From Past To Path: Masked History Learning for Next-Item Prediction in Generative Recommendation

arXiv:2509.23649v2 Announce Type: replace-cross Abstract: Generative recommendation, which directly generates item identifiers, has emerged as a promising paradigm for recommendation systems. However,

FryNet: Dual-Stream Adversarial Fusion for Non-Destructive Frying Oil Oxidation Assessment

arXiv:2604.21321v1 Announce Type: new Abstract: Monitoring frying oil degradation is critical for food safety, yet current practice relies on destructive wet-chemistry assays that provide no spatial i

Full-Body Dynamic Safety for Robot Manipulators: 3D Poisson Safety Functions for CBF-Based Safety Filters

arXiv:2604.21189v1 Announce Type: new Abstract: Collision avoidance for robotic manipulators requires enforcing full-body safety constraints in high-dimensional configuration spaces. Control Barrier F

GFlowState: Visualizing the Training of Generative Flow Networks Beyond the Reward

arXiv:2604.21830v1 Announce Type: new Abstract: We present GFlowState, a visual analytics system designed to illuminate the training process of Generative Flow Networks (GFlowNets or GFNs). GFlowNets

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Han Dan Xue Bu (Mimicry) or Qing Chu Yu Lan (Mastery)? A Cognitive Perspective on Reasoning Distillation in Large Language Models

arXiv:2601.05019v2 Announce Type: replace-cross Abstract: Recent Large Reasoning Models trained via reinforcement learning exhibit a 'natural' alignment with human cognitive costs. However, we show th

HARBOR: Automated Harness Optimization

arXiv:2604.20938v1 Announce Type: cross Abstract: Long-horizon language-model agents are dominated, in lines of code and in operational complexity, not by their underlying model but by the harness tha

Hi-WM: Human-in-the-World-Model for Scalable Robot Post-Training

arXiv:2604.21741v1 Announce Type: new Abstract: Post-training is essential for turning pretrained generalist robot policies into reliable task-specific controllers, but existing human-in-the-loop pipe

Hierarchical Policy Optimization for Simultaneous Translation of Unbounded Speech

arXiv:2604.21045v1 Announce Type: new Abstract: Simultaneous speech translation (SST) generates translations while receiving partial speech input. Recent advances show that large language models (LLMs

How English Print Media Frames Human-Elephant Conflicts in India

arXiv:2604.21496v1 Announce Type: new Abstract: Human-elephant conflict (HEC) is rising across India as habitat loss and expanding human settlements force elephants into closer contact with people. Wh

How to Allocate, How to Learn? Dynamic Rollout Allocation and Advantage Modulation for Policy Optimization

arXiv:2602.19208v2 Announce Type: replace-cross Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has proven effective for Large Language Model (LLM) reasoning, yet current methods face

How VLAs (Really) Work In Open-World Environments

arXiv:2604.21192v1 Announce Type: cross Abstract: Vision-language-action models (VLAs) have been extensively used in robotics applications, achieving great success in various manipulation problems. Mo

Identifying Bias in Machine-generated Text Detection

arXiv:2512.09292v2 Announce Type: replace-cross Abstract: The meteoric rise in text generation capability has been accompanied by parallel growth in interest in machine-generated text detection: the c

In 1982, Blade Runner was speculation. In 2026, it's the conversation. Memory, personhood, alignment, what we owe the minds we build. Watchi…

In 1982, Blade Runner was speculation. In 2026, it's the conversation. Memory, personhood, alignment, what we owe the minds we build. Watching it Thursday 28 May, Kensington Central Library. Come thin

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Inferring High-Level Events from Timestamped Data: Complexity and Medical Applications

arXiv:2604.21793v1 Announce Type: new Abstract: In this paper, we develop a novel logic-based approach to detecting high-level temporally extended events from timestamped data and background knowledge

KD-CVG: A Knowledge-Driven Approach for Creative Video Generation

arXiv:2604.21362v1 Announce Type: new Abstract: Creative Generation (CG) leverages generative models to automatically produce advertising content that highlights product features, and it has been a si

Language-Conditioned Safe Trajectory Generation for Spacecraft Rendezvous

arXiv:2512.09111v3 Announce Type: replace-cross Abstract: Reliable real-time trajectory generation is essential for future autonomous spacecraft. While recent progress in nonconvex guidance and contro

Lawmakers and lobbyists say the Trump administration has lobbied against legislation that would regulate AI in at least six Republican-led states (Amrith Ramkumar/Wall Street Journal)

Amrith Ramkumar / Wall Street Journal: Lawmakers and lobbyists say the Trump administration has lobbied against legislation that would regulate AI in at least six Republican-led states — ‘I am disappo

safetytechmeme

Learning Dynamic Representations and Policies from Multimodal Clinical Time-Series with Informative Missingness

arXiv:2604.21235v1 Announce Type: cross Abstract: Multimodal clinical records contain structured measurements and clinical notes recorded over time, offering rich temporal information about the evolut

Learning Physics from Pretrained Video Models: A Multimodal Continuous and Sequential World Interaction Models for Robotic Manipulation

arXiv:2603.00110v2 Announce Type: replace Abstract: The scarcity of large-scale robotic data has motivated the repurposing of foundation models from other modalities for policy learning. In this work,

Local Neighborhood Instability in Parametric Projections: Quantitative and Visual Analysis

arXiv:2604.21617v1 Announce Type: new Abstract: Parametric projections let analysts embed new points in real time, but input variations from measurement noise or data drift can produce unpredictable s

Locating acts of mechanistic reasoning in student team conversations with mechanistic machine learning

arXiv:2604.21870v1 Announce Type: cross Abstract: STEM education researchers are often interested in identifying moments of students' mechanistic reasoning for deeper analysis, but have limited capaci

safetyarxiv-cs-lg

Logic Jailbreak: Efficiently Unlocking LLM Safety Restrictions Through Formal Logical Expression

arXiv:2505.13527v3 Announce Type: replace-cross Abstract: Despite substantial advancements in aligning large language models (LLMs) with human values, current safety mechanisms remain susceptible to j

Machine Behavior in Relational Moral Dilemmas: Moral Rightness, Predicted Human Behavior, and Model Decisions

arXiv:2604.21871v1 Announce Type: new Abstract: Human moral judgment is context-dependent and modulated by interpersonal relationships. As large language models (LLMs) increasingly function as decisio

Measuring and Exploiting Contextual Bias in LLM-Assisted Security Code Review

arXiv:2603.18740v2 Announce Type: replace-cross Abstract: Automated Code Review (ACR) systems integrating Large Language Models (LLMs) are increasingly adopted in software development workflows, rangi

Mind the Prompt: Self-adaptive Generation of Task Plan Explanations via LLMs

arXiv:2604.21092v1 Announce Type: new Abstract: Integrating Large Language Models (LLMs) into complex software systems enables the generation of human-understandable explanations of opaque AI processe

Mitigating Lost in Multi-turn Conversation via Curriculum RL with Verifiable Accuracy and Abstention Rewards

arXiv:2510.18731v2 Announce Type: replace-cross Abstract: Large Language Models demonstrate strong capabilities in single-turn instruction following but suffer from Lost-in-Conversation (LiC), a degra

Modulating Cross-Modal Convergence with Single-Stimulus, Intra-Modal Dispersion

arXiv:2604.21836v1 Announce Type: cross Abstract: Neural networks exhibit a remarkable degree of representational convergence across diverse architectures, training objectives, and even data modalitie

Multimodal Protein Language Models for Enzyme Kinetic Parameters: From Substrate Recognition to Conformational Adaptation

arXiv:2603.12845v2 Announce Type: replace Abstract: Predicting enzyme kinetic parameters quantifies how efficiently an enzyme catalyzes a specific substrate under defined biochemical conditions. Canon

New episode of The Information Bottleneck is out, this time with @liuzhuang1234 (Princeton). We talked about ConvNeXt and whether architectu…

New episode of The Information Bottleneck is out, this time with @liuzhuang1234 (Princeton). We talked about ConvNeXt and whether architecture still matters; dataset bias and what 'good data' actually

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Participation and Representation in Local Government Speech

arXiv:2604.21202v1 Announce Type: cross Abstract: Local government meetings are the most common formal channel through which residents speak directly with elected officials, contest policies, and shap

Preserving Decision Sovereignty in Military AI: A Trade-Secret-Safe Architectural Framework for Model Replaceability, Human Authority, and State Control

arXiv:2604.20867v1 Announce Type: cross Abstract: Recent events surrounding the relationship between frontier AI suppliers and national-security customers have made a structural problem newly visible:

Probabilistic Verification of Neural Networks via Efficient Probabilistic Hull Generation

arXiv:2604.21556v1 Announce Type: new Abstract: The problem of probabilistic verification of a neural network investigates the probability of satisfying the safe constraints in the output space when t

Quotient-Space Diffusion Models

arXiv:2604.21809v1 Announce Type: cross Abstract: Diffusion-based generative models have reformed generative AI, and have enabled new capabilities in the science domain, for example, generating 3D str

Ramen: Robust Test-Time Adaptation of Vision-Language Models with Active Sample Selection

arXiv:2604.21728v1 Announce Type: new Abstract: Pretrained vision-language models such as CLIP exhibit strong zero-shot generalization but remain sensitive to distribution shifts. Test-time adaptation

Refining Covariance Matrix Estimation in Stochastic Gradient Descent Through Bias Reduction

arXiv:2604.21203v1 Announce Type: cross Abstract: We study online inference and asymptotic covariance estimation for the stochastic gradient descent (SGD) algorithm. While classical methods (such as p

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Reinforcement Learning with Foundation Priors: Let the Embodied Agent Efficiently Learn on Its Own

arXiv:2310.02635v5 Announce Type: replace-cross Abstract: Reinforcement learning (RL) is a promising approach for solving robotic manipulation tasks. However, it is challenging to apply the RL algorit

RELOOP: Recursive Retrieval with Multi-Hop Reasoner and Planners for Heterogeneous QA

arXiv:2510.20505v4 Announce Type: replace-cross Abstract: Retrieval-augmented generation (RAG) remains brittle on multi-step questions and heterogeneous evidence sources, trading accuracy against late