safety

arXiv:2605.07257v1 Announce Type: new Abstract: Generative personalization often suffers from the semantic collapsing problem (SCP), where a learned personalized concept overpowers the rest of the tex

arXiv:2605.06717v1 Announce Type: cross Abstract: Coding agents are rapidly changing the landscape of software development, moving from inline completion to autonomous systems that edit repositories,

Ah yes, chatGPT, tell me more about the 'colony mind', 'foraging patrol' and 'nost building' behaviors of the human liver. For anyone predicting near-term physician replacement by LLM-based AI, please

AI existential crisis for software engineers is to go live in the woods and read poetry. AI existential crisis for creatives is to make things until 4am every day because you can't stop now. is this w

Am old enough to remember when @GeoffreyHinton told me I was stupid for saying that LLMs regurgitate training data. He was wrong. LLM regurgitation is now one of the best-established findings in the f

arXiv:2605.07825v1 Announce Type: cross Abstract: Training multimodal large language models has long been limited by the scarcity of high-quality paired multimodal data. Recent studies show that the s

arXiv:2605.07786v1 Announce Type: cross Abstract: As generative models achieve unprecedented visual quality, the gold standard for image evaluation remains traditional feature-distribution metrics (e.

arXiv:2605.07837v1 Announce Type: cross Abstract: Decision Trees (DTs) are widely used in safety-critical domains such as medical diagnosis, valued for their interpretability and effectiveness on tabu

arXiv:2601.18681v2 Announce Type: replace-cross Abstract: We consider time discretization for score-based diffusion models to generate samples from a learned reverse-time dynamic on a finite grid. Uni

arXiv:2604.01878v2 Announce Type: replace-cross Abstract: Spectral graph contrastive learning often constructs low- and high-frequency views to capture complementary graph signals, but these views are

arXiv:2605.06387v2 Announce Type: replace-cross Abstract: On-policy distillation (OPD) trains a student on its own trajectories with token-level teacher feedback and often outperforms off-policy disti

arXiv:2512.05439v2 Announce Type: replace Abstract: As large language models (LLMs) transition from research prototypes to production systems, practitioners often need reliable methods to verify model

arXiv:2512.23694v2 Announce Type: replace-cross Abstract: Reliable long-horizon value prediction is difficult in offline reinforcement learning because fitted value methods combine bootstrapping, func

arXiv:2605.06879v1 Announce Type: new Abstract: Protein sequence data from nature exhibits survivorship bias: we only observe data from those organisms that survive and reproduce, while non-functional

arXiv:2605.07806v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly used in settings where reliable self-assessment is critical. Assessing model reliability has evolved fro

arXiv:2605.07883v1 Announce Type: new Abstract: Large Language Models (LLMs) rely on safety alignment to obey safe requests while refusing harmful ones. However, traditional refusal mechanisms often l

arXiv:2605.08037v1 Announce Type: cross Abstract: Direct Preference Optimization (DPO) aligns language models using pairwise preference comparisons, offering a simple and effective alternative to Rein

arXiv:2602.23811v4 Announce Type: replace-cross Abstract: We investigate the theoretical aspects of offline reinforcement learning (RL) under general function approximation. While prior works (e.g., X

arXiv:2605.06939v1 Announce Type: new Abstract: LLM-as-a-Judge evaluation has become a standard tool for assessing base model performance. However, characterizing performance via the naive estimator,

🚨BREAKING: Ilya just confirmed under oath what he saw: Sam lying. And he confirmed that he thought it was appropriate to fire Altman for it. And that he had been concerned for about Sam for a long tim

arXiv:2509.08318v2 Announce Type: replace Abstract: Early-exit cascades over a frozen convolutional backbone enable adaptive inference but suffer from three sources of train-inference mismatch: branch

arXiv:2601.21699v2 Announce Type: replace Abstract: Multi-turn reasoning agents solve complex questions by decomposing them into intermediate retrieval or tool-use steps, for accumulating supporting e

arXiv:2605.07065v1 Announce Type: cross Abstract: Individual treatment effects are not point-identified from data. The Probability of Necessity and Sufficiency (PNS) circumvents this limitation by cha

Checkmate to everyone on X who doubted my coverage of Sam’s firing. Checkmate. 🚨BREAKING: Ilya just confirmed under oath what he saw: Sam lying. And he confirmed that he thought it was appropriate to

arXiv:2605.07040v1 Announce Type: cross Abstract: Large language models (LLMs) are widely used for tutoring, feedback generation, and content creation, but their broad pretraining makes them hard to c

Compute is scarce and has to be bought years in advance, before companies know whether revenue will ever catch up. That is exactly the nature of the largest gamble in history. Heaven help the global e

arXiv:2605.06720v1 Announce Type: cross Abstract: Antibody therapeutics are among the most successful modern medicines, yet computationally designing antibodies with desirable binding and developabili

arXiv:2605.07353v1 Announce Type: new Abstract: Large reasoning models often reach correct answers through flawed intermediate steps, creating a gap between final accuracy and reasoning reliability. E

arXiv:2605.07115v1 Announce Type: new Abstract: Stochastic bandit algorithms are usually analyzed under a mean-reward criterion, yet many problems favor arms with strong upper-tail performance, which

arXiv:2603.05687v3 Announce Type: replace Abstract: Contact-rich dexterous manipulation with multi-finger hands remains an open challenge in robotics because task success depends on multi-point contac

arXiv:2605.07171v1 Announce Type: new Abstract: The classic multi-armed bandit (MAB) problem tackles the challenge of accruing maximum reward while making decisions under uncertainty. However, in appl

arXiv:2605.07724v1 Announce Type: cross Abstract: Recursive retraining of generative models poses a critical representation challenge: when synthetic outputs are curated based on a fixed reward signal

arXiv:2605.07314v1 Announce Type: cross Abstract: Knowledge Graphs (KGs) have proven highly effective for recommendation systems by capturing latent item relationships, while recent integration of Lar

Dear @geoffreyhinton, I literally never said that AI systems “JUST regurgitate”; that’s plainly false. I don’t believe it, and I didn’t say it. (They do *sometimes* regurgitate, and the evidence for t

arXiv:2605.06971v1 Announce Type: cross Abstract: Classical optimization theory largely focuses on fixed objective functions, whereas many modern learning systems operate in dynamic environments where

arXiv:2605.07074v1 Announce Type: new Abstract: Detecting AI-generated images across unseen architectures remains challenging, as existing models often overfit to generator-specific fingerprints and s

arXiv:2512.17129v2 Announce Type: replace Abstract: Biological systems can form complex three-dimensional structures through the collective behavior of agents that share a common update rule and opera

arXiv:2605.07674v1 Announce Type: cross Abstract: Regulatory audits of AI systems increasingly rely on differential privacy (DP) to protect training data and model internals. We study audit design whe

arXiv:2605.07503v1 Announce Type: new Abstract: Efficiently aligning large-scale video diffusion models with human intent requires a scalable and trajectory-aware pathway that bridges the inherent dis

arXiv:2602.22831v2 Announce Type: replace-cross Abstract: Moral benchmarks for LLMs typically score models on context-free prompts, implicitly treating the measured choice rate as stable. We test this

arXiv:2602.16928v3 Announce Type: replace-cross Abstract: Much of the advancement in Multi-Agent Reinforcement Learning (MARL) for imperfect-information games has historically depended on the manual,

arXiv:2605.07483v1 Announce Type: cross Abstract: Successful deep neural networks discover salient features of data. We show when and why they fail to learn out-of-distribution (OOD)-relevant represen

arXiv:2605.06885v1 Announce Type: cross Abstract: Diffusion language models (DLMs) have recently demonstrated capabilities that complement standard autoregressive (AR) models, particularly in non-sequ

arXiv:2605.07041v1 Announce Type: cross Abstract: This paper introduces Dr-BA, a first-of-its-kind radar bundle adjustment (BA) framework that operates directly on 2D spinning radar intensity images.

arXiv:2605.07063v1 Announce Type: cross Abstract: Data selection methods address a critical challenge in LLM post-training: effectively leveraging scarce, high-fidelity target data alongside abundant

arXiv:2510.00253v2 Announce Type: replace Abstract: Smoothness is a key inductive bias in machine learning and is closely related to generalization. Existing smoothness-inducing methods typically rely