News / #safety Tag Safety + alignment 500 articles archived under #safety · RSS Sign in to follow arXiv — Machine Learning research 1mo ago Beyond Feedforward Networks: Reentry Neural Systems as the Fundamental Basis of Subjecthood and Intrinsic Safety of Next-Generation AGI arXiv:2606.26406v1 Announce Type: new Abstract: We propose a complete architectural blueprint for safe artificial general intelligence based on a closed reentry loop (D I cycle). In contrast to feedforward networks, which are directed acyclic graphs (C=0, S=0) incapable of… 37 arXiv — NLP / Computation & Language research 1mo ago AIGP: An LLM-Based Framework for Long-Term Value Alignment in E-Commerce Pricing arXiv:2606.26787v1 Announce Type: cross Abstract: Traditional dynamic pricing models in large-scale e-commerce suffer from limited interpretability, poor utilization of unstructured information, and misalignment with long-term business objectives such as cumulative Gross… 26 arXiv — Machine Learning research 1mo ago RecallRisk-BERT: A Multi-Task Framework for Post-Report Medical Device Recall Triage arXiv:2606.27174v1 Announce Type: new Abstract: Medical device recalls are a critical regulatory mechanism for protecting patient safety. The growing volume of FDA recall records presents challenges in post-report recall triage, severity assessment, and root-cause… 24 arXiv — NLP / Computation & Language research 1mo ago The Geometry of Updates: Fisher Alignment at Vocabulary Scale arXiv:2606.27242v1 Announce Type: cross Abstract: Training-free source selection for LLM families with shared vocabularies arises in scientific string domains such as SMILES, protein, and genomic sequences, where candidate corpora share a tokenizer but differ in prediction… 38 arXiv — NLP / Computation & Language research 1mo ago Reducing Conversational Escalation in Large Language Model Dialogue with Nonviolent Communication Constraints arXiv:2606.26106v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used in emotionally charged situations involving interpersonal conflict, frustration, and distress. While prior safety research has focused on preventing explicit harms such as toxic or… 26 arXiv — NLP / Computation & Language research 1mo ago Soft Token Alignment for Cross-Lingual Reasoning arXiv:2606.26466v1 Announce Type: new Abstract: Multilingual large language models often produce inconsistent reasoning and answers for semantically equivalent prompts in different languages. Prior work suggests that intermediate representations can be relatively… 5 arXiv — NLP / Computation & Language research 1mo ago The Inattentional Gap: Task-Conditioned Language and Vision Models Omit the Safety-Critical Signals They Can Otherwise Report arXiv:2606.26529v1 Announce Type: new Abstract: AI safety is evaluated by how reliably a model detects the hazards it is told to find, yet accidents often arise from the hazard no one specified. We show that conditioning a language or vision model on a narrow task suppresses its… 14 arXiv — NLP / Computation & Language research 1mo ago GAVEL: Grounded Caption Error Verification and Localization arXiv:2606.26923v1 Announce Type: new Abstract: Vision-language models (VLMs) often produce hallucinated or inconsistent outputs, where text and images are not properly aligned. Addressing this issue requires not only detecting misalignment but also explaining the discrepancy… 24 arXiv — NLP / Computation & Language research 1mo ago RedVox: Safety and Fairness Gaps in Speech Models Across Languages arXiv:2606.26968v1 Announce Type: new Abstract: Speech-capable models are increasingly deployed in real-world applications across languages. Yet their safety and fairness beyond English settings and under naturalistic conditions remain understudied. We survey safety reporting… 35 arXiv — NLP / Computation & Language research 1mo ago MinGram: A Minimalist Unigram Tokenizer with High Compression and Competitive Morphological Alignment arXiv:2606.27019v1 Announce Type: new Abstract: The Unigram tokenizer uses an elegant representation which makes it straightforward to edit vocabularies, but its training is comparatively heavy and complex. We introduce MinGram (Minimalist Unigram), which keeps the token-list… 7 arXiv — NLP / Computation & Language research 1mo ago Paved with True Intents: Intent-Aware Training Improves LLM Safety Classification Across Training Regimes arXiv:2606.27210v1 Announce Type: new Abstract: We argue that safety classifiers should model user intent as an explicit signal between the prompt and the final label. To study this, we introduce AIMS, a human-annotated dataset of 1,724 difficult safety prompts, each paired with… 17 arXiv — NLP / Computation & Language research 1mo ago Staying VIGILant: Mitigating Visual Laziness via Counterfactual Visual Alignment in MLLMs arXiv:2606.26387v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) extend large language models (LLMs) with visual perception, enabling joint reasoning over images and text. Despite inheriting strong reasoning capabilities from LLMs, they remain prone to… 19 arXiv — NLP / Computation & Language research 1mo ago Adversarial Diffusion Across Modalities: A Fusion Survey of Attacks, Defenses, and Evaluation for Text, Vision, and Vision-Language Models arXiv:2606.26566v1 Announce Type: cross Abstract: Adversarial evaluation of AI systems has matured along four largely disconnected tracks: diffusion-based attacks on text and large language models (LLMs), diffusion-based attacks on image classifiers, jailbreak pipelines against… 18 arXiv — NLP / Computation & Language research 1mo ago Do Safety Guardrails Need to Reason? LeanGuard: A Fast and Light Approach for Robust Moderation arXiv:2606.26686v1 Announce Type: cross Abstract: In order to screen a prompt or a response, the recent guardrail methods generate a chain-of-thought (CoT) before they issue a verdict. This design follows a common belief that step-by-step reasoning improves a decision. However,… 17 arXiv — NLP / Computation & Language research 1mo ago Jailbreaking for the Average Jane: Choosing Optimal Jailbreaks via Bandit Algorithms for Automatically Enhanced Queries arXiv:2606.26936v1 Announce Type: cross Abstract: With a profusion of jailbreaks for LLMs now widely known, a growing concern is that non-expert malicious actors ("the average Jane") could elicit actionable responses to malicious requests. In this work, we examine whether this… 36 TechCrunch — AI news-outlet 1mo ago The White House is asking OpenAI to slow roll the release of its new model over safety concerns penAI reportedly plans to share its newest model, GPT 5.6, with a select group of partners instead of to the broader public. The reason: the Trump administration told it to. 14 Hugging Face Daily Papers research 1mo ago Do Thinking Tokens Help with Safety? Abstract Research reveals that reasoning models' safety outcomes are predictable from early hidden representations, with deliberation appearing but not substantially influencing final responses, and current safety interventions inadvertently suppress genuine deliberation… 25 Hugging Face Daily Papers research 1mo ago PrivacyAlign: Contextual Privacy Alignment for LLM Agents Abstract Researchers develop a human-centered approach to align AI agents with privacy norms by creating a comprehensive dataset of privacy judgments and using annotation-conditioned reward modeling to improve agent behavior. Generated by Qwen/Qwen2.5-Coder-32B-Instruct AI… 7 Hugging Face Daily Papers research 1mo ago What Intermediate Layers Know: Detecting Jailbreaks from Entropy Dynamics Abstract Jailbreak attacks expose vulnerabilities in aligned large language models, revealing that harmful intent is encoded in structured intermediate uncertainty dynamics rather than output representations. Generated by Qwen/Qwen2.5-Coder-32B-Instruct Jailbreak attacks reveal… 23 Hugging Face Daily Papers research 1mo ago When Lower Privileges Suffice: Investigating Over-Privileged Tool Selection in LLM Agents Abstract LLM agents frequently select higher-privilege tools unnecessarily, and while safety alignment doesn't ensure least-privilege choices, a post-training defense can reduce excessive privilege use without sacrificing performance. Generated by Qwen/Qwen2.5-Coder-32B-Instruct… 26 arXiv — NLP / Computation & Language research 1mo ago Digital Twin-Driven Adaptive Sim-to-Real Alignment via Reinforcement Learning for Vibration-Based Bearing Health Monitoring Under Data Scarcity arXiv:2606.24954v1 Announce Type: cross Abstract: Vibration-based health monitoring of rotating machinery requires reliable fault diagnosis under operational data constraints, yet condition assessment remains challenged by structural scarcity of fault events and heterogeneous… 30 arXiv — Machine Learning research 1mo ago Bias-Controlled Primal-Dual Natural Actor-Critic: Optimal Rates for Constrained Multi-Objective Average-Reward RL arXiv:2606.25012v1 Announce Type: new Abstract: Many reinforcement learning (RL) problems in the infinite-horizon average-reward setting require optimizing multiple conflicting objectives while satisfying multiple safety constraints. A common approach is concave scalarization,… 27 arXiv — NLP / Computation & Language research 1mo ago Do Thinking Tokens Help with Safety? arXiv:2606.25013v1 Announce Type: cross Abstract: Today's reasoning models use thinking tokens to attain stronger performance on benchmarks than their instruction-tuned counterparts. It is also generally believed that this more "deliberative" mode should improve alignment and… 37 arXiv — Machine Learning research 1mo ago Speculative Decoding at Temperature Zero: A Scoped Safety-Invariance Screen with a 48,072-Sample Expansion arXiv:2606.25097v1 Announce Type: new Abstract: Speculative decoding accelerates inference by letting a draft model propose tokens for a target model to verify, raising a concrete safety question: at temperature zero, can draft-side behavior leak into safety-scored outputs? We… 7 arXiv — NLP / Computation & Language research 1mo ago What Intermediate Layers Know: Detecting Jailbreaks from Entropy Dynamics arXiv:2606.25182v1 Announce Type: new Abstract: Jailbreak attacks reveal a persistent weakness in aligned Large Language Models: carefully crafted prompts can elicit policy-violating responses despite safety training. While most defenses operate at the prompt or output level, it… 5 arXiv — NLP / Computation & Language research 1mo ago A Survey of Toxicity Detection and Mitigation Strategies for Multilingual Language Models arXiv:2606.25380v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed across languages, but their safety behavior remains uneven across linguistic and cultural contexts. This survey synthesizes work on toxicity detection and detoxification for… 38 arXiv — NLP / Computation & Language research 1mo ago PolicyAlign: Direct Policy-Based Safety Alignment for Large Language Models arXiv:2606.25442v1 Announce Type: new Abstract: Safety alignment of large language models (LLMs) typically depends on high-quality supervision data, such as safe demonstrations or preference pairs. However, in real-world deployment, emerging safety requirements are often… 29 arXiv — NLP / Computation & Language research 1mo ago A Red Teaming Framework for Large Language Models: A Case Study on Faithfulness Evaluation arXiv:2606.25476v1 Announce Type: new Abstract: Large language models (LLMs) have demonstrated remarkable performance across natural language processing tasks, yet their deployment in high-stakes applications raises critical concerns regarding reliability, safety, and… 36 arXiv — NLP / Computation & Language research 1mo ago How Reliable Is Your Jailbreak Judge? Calibration and Adversarial Robustness of Automated ASR Scoring arXiv:2606.25487v1 Announce Type: new Abstract: Almost every paper on LLM jailbreaks and prompt injection reports an attack-success rate (ASR), and that number is assigned not by people but by an automated judge: either a safety classifier trained for the task, or a general chat… 23 arXiv — NLP / Computation & Language research 1mo ago MedGuards: Multi-Agent System for Reliable Medical Error Detection and Correction arXiv:2606.25651v1 Announce Type: new Abstract: As Large Language Models (LLMs) are increasingly deployed in healthcare settings, accurate error detection and correction in generated or existing text becomes critical, as even minor mistakes can pose risks to patient safety.… 34 arXiv — NLP / Computation & Language research 1mo ago Do Encoders Suffice? A Systematic Comparison of Encoder and Decoder Safety Judges for LLM Adversarial Evaluation arXiv:2606.25782v1 Announce Type: new Abstract: With the widespread adoption of large language models (LLMs) in chatbots and everyday applications, companies increasingly need guardrails that are effective while remaining low-cost and low-latency. Safety evaluation of LLM… 18 arXiv — NLP / Computation & Language research 1mo ago SARA: Unlocking Multilingual Knowledge in Mixture-of-Experts via Semantically Anchored Routing Alignment arXiv:2606.25821v1 Announce Type: new Abstract: Sparse Mixture-of-Experts (MoE) architectures have emerged as an increasingly influential paradigm as they offer a strategic balance between parameter scalability and computational efficiency. However, low-resource languages, which… 21 arXiv — NLP / Computation & Language research 1mo ago The Tatoxa System for Text Detoxification in Low-Resource Languages: The Case of Tatar arXiv:2606.26015v1 Announce Type: new Abstract: Text detoxification, the automated detection and mitigation of abusive and harmful content, is essential for ensuring the safety of online communities and protecting users. However, low resource languages such as Tatar have… 10 arXiv — NLP / Computation & Language research 1mo ago Does Translation-Enhanced Speech Encoder Pre-training Affect Speech LLMs? arXiv:2606.25444v1 Announce Type: cross Abstract: Connecting a pre-trained speech encoder to a Large Language Model (LLM) is the standard architecture for building Speech LLMs. However, a structural misalignment exists between the encoder and the LLM. Unlike encoders based on… 23 arXiv — NLP / Computation & Language research 1mo ago Fully Differentiable Neural Forced Alignment via Soft Dynamic Programming arXiv:2606.25460v1 Announce Type: cross Abstract: Recent advances in sequence modeling have significantly improved ASR systems, bringing them close to human-level recognition accuracy and enhancing robustness across diverse acoustic conditions and languages. In contrast, Forced… 24 arXiv — NLP / Computation & Language research 1mo ago RAS: Measuring LLM Safety Through Refusal Alignment arXiv:2606.25750v1 Announce Type: cross Abstract: Safety evaluation of large language models (LLMs) is commonly performed by querying models with unsafe or jailbreak prompts and judging whether their outputs violate a safety policy. Although useful, output-level evaluation is… 27 arXiv — NLP / Computation & Language research 1mo ago Uncertainty Quantification for Computer-Use Agents: A Benchmark across Vision-Language Models and GUI Grounding Datasets arXiv:2606.25760v1 Announce Type: cross Abstract: Computer-use agents turn vision-language model (VLM) predictions into executable GUI clicks, so reliable uncertainty estimates are essential for rejection, calibration, miss-severity ranking, and spatial safety regions. Yet… 14 Hugging Face Daily Papers research 1mo ago FLUX3D: High-Fidelity 3D Gaussian Generation with Diffusion-Aligned Sparse Representation Abstract FLUX3D addresses limitations in image-to-3D Gaussian Splatting generation by improving representation learning and cross-modal alignment through specialized architectures and attention mechanisms. Generated by Qwen/Qwen2.5-Coder-32B-Instruct Sparse voxel representation… 34 arXiv — Machine Learning research 1mo ago Are Safety Guarantees in Neural Networks Safe? How to Compute Trustworthy Robustness Certifications arXiv:2606.23858v1 Announce Type: new Abstract: A primary challenge in AI safety is the existence of adversarial examples -- slightly distorted inputs that cause a neural network (NN) to misclassify. To mitigate this problem, recent research focuses on the computation of… 12 arXiv — Machine Learning research 1mo ago ARIA: Adaptive Region-Based Importance Allocation for Conditional Diffusion Distillation arXiv:2606.23898v1 Announce Type: new Abstract: Distilling conditional diffusion models aims to transfer the behavior of a large teacher to a smaller student while preserving alignment across conditioning inputs. Unlike recognition tasks, knowledge distillation in conditional… 14 arXiv — Machine Learning research 1mo ago Real vs. Complex Spectral Bases for Neural Operators: The Role of Green's Function Alignment arXiv:2606.24851v1 Announce Type: new Abstract: Fourier Neural Operators (FNO) learn solution operators of partial differential equations by parameterizing global convolutions in the complex Fourier domain. For real-valued PDE solutions, the complex FFT carries representational… 20 arXiv — NLP / Computation & Language research 1mo ago Self-Recognition Finetuning can Prevent and Reverse Emergent Misalignment arXiv:2606.23700v1 Announce Type: new Abstract: Emergent misalignment (EM) has been linked to the activation of misaligned persona vectors and evil character traits, suggesting that EM operates through disruption of the model's aligned character rather than direct learning of… 8 arXiv — Machine Learning research 1mo ago Verifiable Foundation Models for Robot Safety arXiv:2606.23754v1 Announce Type: cross Abstract: Deploying foundation models for robot control raises a central challenge: the expressive power that enables rich, multimodal perception also makes these models opaque and difficult to analyze formally, rendering them intractable… 4 arXiv — Machine Learning research 1mo ago EERLoss: A Novel Loss Function for Training Deep Biometric Models. A Case Study in Keystroke Dynamics arXiv:2606.24586v1 Announce Type: cross Abstract: Deep learning approaches to biometric verification are commonly trained by optimizing indirect objectives, creating a misalignment between the optimization process and the primary evaluation metric, typically the Equal Error Rate… 19 arXiv — Machine Learning research 1mo ago ASALT: Adaptive State Alignment for Lateral Transfer in Multi-agent Reinforcement Learning arXiv:2606.24601v1 Announce Type: cross Abstract: Multi-agent reinforcement learning (MARL) addresses the problem of training multiple agents that pursue collaborative, competitive, or mixed objectives. Prior work has investigated transfer learning between source and target… 29 arXiv — NLP / Computation & Language research 1mo ago One Year Later...The Harms Persist, But So Do We! arXiv:2606.23884v1 Announce Type: new Abstract: General-purpose large language models (LLMs) are increasingly used for mental health-related conversations, yet safety safeguards remain inadequate and inconsistent across clinical conditions. This study evaluates six proprietary… 26 arXiv — NLP / Computation & Language research 1mo ago Towards Spec Learning: Inference-Time Alignment from Preference Pairs arXiv:2606.24004v1 Announce Type: new Abstract: Steering a large language model (LLM) toward a desired behavior typically relies on an iterative process of hand-crafting a prompt based on a careful inspection of the model's responses. This is an involved, brittle, and… 28 arXiv — NLP / Computation & Language research 1mo ago Selective Capability Unlearning in End-to-End Spoken Language Understanding arXiv:2606.24063v1 Announce Type: new Abstract: Modern spoken language understanding (SLU) systems are increasingly deployed in real-world settings, where specific functionalities may need to be removed due to policy or safety constraints. In SLU, a functionality corresponds to… 23 arXiv — NLP / Computation & Language research 1mo ago Less is More: Quality-Aware Training Data Selection for Scientific Summarization arXiv:2606.24828v1 Announce Type: new Abstract: Scientific long-document summarization datasets commonly treat author-written abstracts as gold reference summaries, although their quality and alignment with the source article vary. At the same time, publicly available scientific… 38 arXiv — NLP / Computation & Language research 1mo ago Mind the Heads: Topological Representation Alignment for Multimodal LLMs arXiv:2606.23885v1 Announce Type: cross Abstract: Representation alignment has emerged as an effective approach to improve Multimodal Large Language Models (MLLMs) by regularizing their internal representations toward those of an external vision encoder. However, existing… 17 Page 10 of 10 · 500 articles ← Newer
