Search: “safety”

Learning between the peaks: sharp asymptotics for kernel ridge regression under power-law anisotropy

arXiv:2608.28564v1 Announce Type: cross Abstract: We study kernel ridge regression under anisotropic Gaussian data, where the input covariance decays as a power law with exponent alphageq 0 for polyno

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Leveraging a Foundation Model for the EEG-Based Diagnosis of Alzheimer's Disease

DGX agent

arXiv:2608.27719v1 Announce Type: new Abstract: Biological heterogeneity in Alzheimer's Disease (AD) poses a critical diagnostic challenge, particularly for traditional linear methods that fail to cap

safetyarxiv-cs-lg

31 Aug 2026

Marginal Coverage Credit Reduces Redundant Exploration in Parallel State-Entropy Optimization

arXiv:2608.27507v1 Announce Type: new Abstract: Policy Gradient for Parallel State Entropy maximization (PGPSE) expands state-space coverage by training independently parameterized policies in replica

Medical Imaging AI Competitions Lack Fairness

arXiv:2512.17581v2 Announce Type: replace Abstract: Benchmarking competitions are central to the development of artificial intelligence (AI) in medical imaging, defining performance standards and shap

More Data Cannot Break a Symmetry: Identifiability by Design

arXiv:2608.27651v1 Announce Type: new Abstract: Unsupervised representational alignment recovers a stimulus-by-stimulus correspondence from geometry alone, but the automorphism group of the stimulus g

Parser States Already Know: Structure-Conditioned KV Persistence for Structured Generation

arXiv:2608.28276v1 Announce Type: new Abstract: Structured generation underpins large language model (LLM) agents that produce JSON, SQL, and function calls, where a single wrong field can cause the d

Personalized and Multi-View Representation for Federated Cold-Start Recommendation

arXiv:2608.27826v1 Announce Type: cross Abstract: Federated recommendation (FedRec) enables personalized modeling without centralizing users' interaction histories, but most existing methods assume a

Phoneme- and Word-Level Metrics Using Self-Supervised Speech Representations for Forced Alignment Evaluation

arXiv:2608.28508v1 Announce Type: new Abstract: Forced alignment evaluation typically requires manually annotated timestamps, limiting large-scale and multilingual analysis. We introduce two corpus-le

PHR-VLA: Planning Horizon Reasoning for Vision-Language-Action Models

arXiv:2608.27609v1 Announce Type: new Abstract: Vision-language-action models (VLAs) have shown strong promise for general-purpose robotic manipulation by mapping language instructions and vision obse

PolicyLong: Towards On-Policy Context Extension

arXiv:2604.07809v2 Announce Type: replace Abstract: Extending LLM context windows is hindered by scarce high-quality long-context data. Recent methods synthesize data with genuine long-range dependenc

Prompts Without Evidence: How Neuroimaging Mentions Shift Clinical Vision-Language Model Predictions

arXiv:2603.28387v3 Announce Type: replace-cross Abstract: Trustworthy clinical AI must use real evidence and avoid relying on surface-level artifacts. We evaluate 12 open-weight vision-language models

Relational Knowledge Distillation Brings DNN Representations Close Enough to Humans to Be Aligned Without Supervision

arXiv:2608.27877v1 Announce Type: new Abstract: Linking the internal representations of deep neural networks (DNNs) to human mental representations is important for using DNNs as computational models

Relax Forcing: Relaxed KV-Memory for Consistent Long Video Generation

arXiv:2603.21366v2 Announce Type: replace Abstract: Autoregressive video diffusion has recently emerged as a promising paradigm for long-video generation, enabling causal synthesis beyond the temporal

REPLICANT: Learning Policies for Evading and Hardening Malware Detectors

arXiv:2608.28499v1 Announce Type: new Abstract: To determine the real-world effectiveness of machine learning based malware detection, it is vital to evaluate its robustness against highly capable adv

Seems concerning: There are major incidents happening, and yet we're ~entirely dependent on AI companies to self-disclose information about …

Seems concerning: There are major incidents happening, and yet we're ~entirely dependent on AI companies to self-disclose information about them. It's not hard to see how this goes badly. While there

Six Dubious Strategies The AI industry Has Tried: Doom (Oh No! AI Civilizations!) Gloom (You will all lose your jobs!) Nerd Rapture (AI will…

Six Dubious Strategies The AI industry Has Tried: Doom (Oh No! AI Civilizations!) Gloom (You will all lose your jobs!) Nerd Rapture (AI will be better than all humans by the end of next year!) Terror

Stay Seated: Learning Omnidirectional Humanoid Locomotion on a Passive Mobile Chair with Casters

arXiv:2608.28090v1 Announce Type: new Abstract: Humanoid robots with quasi-direct-drive actuators continuously generate joint torque while standing, whereas seated humans delegate weight support to ch

SymboLLM-FE: LLM-Accelerated Symbolic Regression for Automated Feature Engineering on Tabular Data

arXiv:2608.28408v1 Announce Type: new Abstract: Tabular data, as a core data format in machine learning, often lacks the discriminative power needed for high-performance modeling due to insufficient f

Temporal Memory-Aware Online Test-Time Adaptation on Dynamic Graphs

arXiv:2608.27948v1 Announce Type: new Abstract: Test-time adaptation (TTA) on graphs aims to adapt a graph neural network (GNN) that is well-trained on the training graph to the test graph, which invo

Towards a mathematical theory of superposition

arXiv:2608.27540v1 Announce Type: cross Abstract: We develop a mathematical theory of superposition in neural networks using tools from frame theory and compressed sensing. In our model, a sparse bina

Training-free Suction Grasp Detection for Deformed Aseptic Cartons Using Vision-Language Models and Geometric Surface Scoring

arXiv:2608.28246v1 Announce Type: new Abstract: Robotic sorting of recyclable waste is challenging due to the deformable and geometrically inconsistent nature of target objects. We present a training-

Trajectory balance: Improved credit assignment in GFlowNets

arXiv:2201.13259v4 Announce Type: replace Abstract: Generative flow networks (GFlowNets) are a method for learning a stochastic policy for generating compositional objects, such as graphs or strings,

UIC-AIHealth4All at ArchEHR-QA 2026: Answer-First Evidence Grounding for Clinical Question Answering

arXiv:2608.27467v1 Announce Type: new Abstract: We describe the UIC-AIHealth4All system for ArchEHR-QA 2026, a shared task on grounded question answering from electronic health records. We participate

VISTA: Verifier-Informed Student-to-Teacher Adaptation for On-Policy Self-Distillation

arXiv:2608.28306v1 Announce Type: cross Abstract: On-policy self-distillation (OPSD) improves reasoning by training a problem-only student on its own rollouts using dense token-level supervision from

When Can Conditional Flow Matching Replace Pointwise Negative Log-Likelihood?

arXiv:2608.28010v1 Announce Type: new Abstract: Flow matching enables likelihood-free training, yet alignment methods increasingly reuse conditional flow matching (CFM) losses as endpoint negative log

When Linguistic and Internal Confidence Diverge in Large Language Models

arXiv:2608.28382v1 Announce Type: new Abstract: Users often ask large language models (LLMs) to report how confident they are, but it is unclear whether such linguistic confidence tracks the model's i

When Tokenizers Fail: Byte-Level Chunking for Zero-Shot Transfer to Low-Resource Languages

arXiv:2608.27658v1 Announce Type: new Abstract: Subword tokenization hinders low-resource language processing by imposing frequency patterns from dominant languages onto script-sharing variants. Byte-

14/ If frontier labs are serious about defense, they should offer cyber models to defenders at cost and stop fighting open weights through t…

14/ If frontier labs are serious about defense, they should offer cyber models to defenders at cost and stop fighting open weights through the regulatory channel. They created the problem, and for muc

I've been genuinely shocked at the complacency here. There's no evidence anyone is really doing anything! Like, that was swell of OpenAI, …

I've been genuinely shocked at the complacency here. There's no evidence anyone is really *doing* anything! Like, that was swell of OpenAI, whose models committed a federal crime, to let some independ

or: they don’t have any more to announce.

or: they don’t have any more to announce. Today I was asked why we haven’t announced more math results from Astra since these problems. At @OpenAI we aim to devote time to finding and announcing math

We will not get through this era of history well if we lose ourselves in anthropomorphism. @anilkseth dissects @dwarkesh_sp’s misleadingly a…

We will not get through this era of history well if we lose ourselves in anthropomorphism. @anilkseth dissects @dwarkesh_sp’s misleadingly anthropomorphic summary of the HF incident: @dwarkesh_sp's su

“What Ilya saw” is what Dario saw which is that Sam can’t be trusted. Which is why they left. A lot of other people have discovered the same…

“What Ilya saw” is what Dario saw which is that Sam can’t be trusted. Which is why they left. A lot of other people have discovered the same, since. If OpenAI is going to IPO or OpenAI has AGI Then Wh

A Geometry-Driven, Framework-Agnostic Optimization for Object Pose Estimation

arXiv:2608.26859v1 Announce Type: new Abstract: Current object pose estimation research remains predominantly model-centric, focusing on architectural innovations and post-processing refinements. This

A Unified Framework for Fair and Personalized Decentralized Learning under Communication Constraints

arXiv:2608.26493v1 Announce Type: new Abstract: Decentralized learning systems aim to collaboratively train models across multiple clients without relying on a central coordinator. While decentralizat

Accurate Measurement of 3D and 2D Circular Centers With Application to LiDAR-Camera Extrinsic Calibration

arXiv:2511.06611v2 Announce Type: replace Abstract: Accurate measurement of circular centers is a fun-damental geometric sensing problem in instrumentation and measurement tasks involving cameras, LiD

Active Preference Learning over Latent Preference Archetypes for Many-Objective Bayesian Optimization

arXiv:2603.28410v2 Announce Type: replace Abstract: Preference-based many-objective Bayesian optimization typically assumes that all pairwise comparisons arise from a single latent utility function, d

Approved Too Late: Verdict Staleness in LLM-Guarded Self-Adaptive Systems

arXiv:2608.26306v1 Announce Type: new Abstract: A large language model (LLM) guardrail for a self-adaptive system (SAS) may issue an approval that is correct at check time but stale by actuation. This

Arrive and Survive: Scaling Safe Goal-Conditioned Policy Learning from One-Bit Failure Signals

arXiv:2608.26571v1 Announce Type: new Abstract: Contrastive reinforcement learning (CRL) scales effectively in goal-conditioned tasks by casting policy learning into a self-supervised contrastive obje

Assessing the Downstream Utility of Evidence-Aware Retrieval in RAG

arXiv:2608.26379v1 Announce Type: cross Abstract: Retrieval evaluation for retrieval-augmented generation (RAG) is increasingly designed around whether retrieved passages contain evidence that can sup

Beyond Atomic Layouts: Compositional Design Understanding with Vision-Language Models

arXiv:2608.26716v1 Announce Type: new Abstract: Layout understanding, or the interpretation of element organization, is essential for document analysis, user interface (UI) creation, and graphic desig

Beyond Client Averaging: A Client-Independent Second-Order Stationary-Bias Component in Stochastic SCAFFOLD

arXiv:2608.26765v1 Announce Type: new Abstract: Existing constant-step analysis of stochastic Scaf{} identifies a leading O(gamma/N) stationary mean bias and shows that higher-order bias can persist a

Boosting LLM Exploration via Weak-Model Guidance in RLVR

arXiv:2608.27420v1 Announce Type: new Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) significantly improves LLM reasoning but often causes a drop in policy entropy, leading to narrowe

CARE: Causally-Aligned Reasoning Exploration for Medical Large Language Models

arXiv:2608.26147v1 Announce Type: new Abstract: Large Language Models (LLMs) have shown strong potential for medical reasoning, yet the scarcity and cost of expert-annotated data constrain their progr

Case2Flow: Bridging Patient Cases and Guideline Flowcharts through Multimodal Retrieval

arXiv:2608.26414v1 Announce Type: new Abstract: Medical guidelines encode rich, evidence-based decision logic, yet the specific decision artifact a clinician needs is hard to locate within a guideline

Categorizer Automata for Discounted-Sum Payoffs

arXiv:2608.26763v1 Announce Type: new Abstract: Categorizing continuous data into discrete bins is a fundamental operation in artificial intelligence. We introduce the categorizer automaton, a determi

CLIPPER: Replayable Shortlisted Optimization for Repeated Spatial Coverage Planning

arXiv:2608.26819v1 Announce Type: new Abstract: Operational requirements developed with the City of Braunschweig frame municipal micromobility planning under geofenced exclusions, mandatory retained s

Communication styles and reader preferences of LLM- and human-authored COVID-19 information explanations: a case study

arXiv:2505.08143v2 Announce Type: replace-cross Abstract: With the wide adoption of large language models (LLMs) in information assistance, it is essential to examine their alignment with human commun

Counterfactual Bias Testing for Application Tracking System

arXiv:2608.26899v1 Announce Type: new Abstract: Automated candidate-job matching systems are increasingly classified as high-risk AI under emerging regulation, yet auditing them for demographic bias i