Knowledge catalogue
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
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
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
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
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 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, whose models committed a federal crime, to let some independ
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 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, since. If OpenAI is going to IPO or OpenAI has AGI Then Wh
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
arXiv:2608.27115v1 Announce Type: new Abstract: Cross-lingual alignment in multilingual language models is typically attributed to joint training: shared parameters, mixed-language batches, or explici
arXiv:2608.26788v1 Announce Type: new Abstract: Recent work shows that pre-trained, instruction-tuned vision-language models (VLMs) perform well at mapping from instructions and observations to high-l
arXiv:2608.27309v1 Announce Type: cross Abstract: Audits of LLM judges certify a bias by contrasting matched conditions, and the strongest designs difference twice: a within-item contrast between two
arXiv:2608.27032v1 Announce Type: new Abstract: Direct Preference Optimization (DPO) is a widely used objective for aligning language models from preference data, with the coefficient eta commonly int
arXiv:2608.26589v1 Announce Type: new Abstract: Image-to-Point Cloud Registration aims to estimate the camera pose of a given image within a 3D scene point cloud, which is a fundamental task in autono
arXiv:2608.27072v1 Announce Type: cross Abstract: A core question in decision-making for agents is whether the relative priorities of competing lower-level objectives can be determined by emotional pr
arXiv:2608.26827v1 Announce Type: new Abstract: Patient-conditioned acquisition policies for ECG lead-channel selection can outperform population-wide fixed protocols by tailoring the channel budget t
arXiv:2608.26696v1 Announce Type: new Abstract: Enterprise deployments of autonomous AI agents inherit a control model built for human users and long-lived services, and the fit fails in three specifi
arXiv:2608.26645v1 Announce Type: new Abstract: Vision-Language-Action Models~(VLAs) have demonstrated significant promise in generalizing to complex, long-horizon robotic manipulation tasks. However,
Gary and I co-wrote this piece about the security standards we should expect from the AI labs. It’s our attempt to bring some clarity to the OpenAI Hugging Face incident. Now live (and free) at Marcus
arXiv:2608.26724v1 Announce Type: new Abstract: Multi-view anomaly detection (MvAD) detects defects by exploiting complementary observations from multiple camera viewpoints. The central challenge is t
arXiv:2608.27079v1 Announce Type: new Abstract: Pretrained vision-language-action (VLA) policies provide strong priors for robot manipulation, yet adapting them online to fine-grained biomedical tasks
arXiv:2608.26585v1 Announce Type: new Abstract: Discrete diffusion models have become a strong, widely adopted class of generators for sequence data, and steering them toward a downstream reward at in
arXiv:2608.26961v1 Announce Type: new Abstract: Graphs are invariant under node permutations, motivating the use of permutation-equivariant architectures in generative models. In flow matching, howeve
arXiv:2608.26804v1 Announce Type: cross Abstract: Recommendation impressions are a finite resource, hence delivering a recommendation to a user who would discover the content organically yields no inc
arXiv:2608.26779v1 Announce Type: new Abstract: Preference learning optimizes models using response pairs, yet the informativeness of these pairs is fundamentally shaped by the instructions from which
arXiv:2608.26137v1 Announce Type: new Abstract: Second-language (L2) English learners can rarely rehearse speaking with a partner. Speaking is also the most anxiety-laden skill. These gaps drive a fas
arXiv:2608.27031v1 Announce Type: new Abstract: Measuring alignment between documents and structured reference frameworks requires identifying conceptual evidence distributed throughout the text and r
just had a quick look at microduck_rl the codebase is very elegant, i’d recommend reading the agent.md since it contains quite a few fun quirks for reward modeling like head tracking too tight impairs
arXiv:2608.27102v1 Announce Type: new Abstract: Large Language Models (LLMs) operate in hospitals, courtrooms, banks, and public service desks, where fluent, confident outputs are treated as authorita
arXiv:2608.26889v1 Announce Type: new Abstract: Learning-augmented algorithms improve online decisions using predictions, but unreliable advice may harm efficiency and fairness. We study an online all
arXiv:2608.26489v1 Announce Type: new Abstract: Detecting woody clearing is vital for managing biodiversity. Deep learning models can detect change in woody vegetation from bitemporal remote sensing i
arXiv:2608.27259v1 Announce Type: new Abstract: World Action Models (WAMs) augment robot policies by predicting how task-relevant scene states may evolve under interaction. Recent WAMs increasingly pe
arXiv:2608.26848v1 Announce Type: cross Abstract: Medical Visual Question Answering (Med-VQA) holds significant promise for clinical decision support, yet faces challenges due to limited annotated dat
arXiv:2510.18383v4 Announce Type: replace-cross Abstract: Distilling the tool-use capabilities of large language models (LLMs) into small language models (SLMs) is essential for their practical applic
arXiv:2608.26171v1 Announce Type: cross Abstract: Multi-stage LLM hiring pipelines (resume improvement, interview question generation, answer feedback) can fabricate credentials, inflate qualifiers, a
arXiv:2503.18856v3 Announce Type: replace Abstract: An important objective in computational biology is the efficient integration of multi-omics data. The task of integration comes with challenges: mul
arXiv:2608.26161v1 Announce Type: cross Abstract: Although Large Language Models (LLMs) demonstrate remarkable capabilities in reasoning and decision-making, high-fidelity probabilistic sampling remai
Now live (and free) at Marcus on AI: takeways on the OpenAI / Hugging Face attack, coauthored with @ZackKorman, focusing not so much on what the AI did as on what OpenAI should have done. https://gary
arXiv:2608.26658v1 Announce Type: cross Abstract: Generative retrieval has demonstrated strong performance by directly generating product semantic identifiers (SIDs). Extending this paradigm to image
arXiv:2608.26190v1 Announce Type: new Abstract: World models enable agents to reason about future outcomes and learn policies from their knowledge of state transition, but existing approaches primaril
arXiv:2608.26735v1 Announce Type: new Abstract: Specializing large language models to vertical domains improves domain-specific behavior but often degrades general capabilities such as reasoning, codi
arXiv:2608.26684v1 Announce Type: new Abstract: Recent large language models achieve strong performance on complex reasoning tasks, where reinforcement learning with Group Relative Policy Optimization
arXiv:2506.21599v5 Announce Type: replace-cross Abstract: Advancing large language models (LLMs) for the next point-of-interest (POI) recommendation task faces two fundamental challenges: (i) although