Knowledge catalogue
arXiv:2610.01165v1 Announce Type: new Abstract: Layer interventions are widely used to probe the internal organization of language models, yet most analyses examine a single training checkpoint even t
arXiv:2610.00737v1 Announce Type: cross Abstract: Personalized image generation has remained narrowly focused on conditional synthesis from curated visual exemplars, rather than capturing who a user i
arXiv:2610.00949v1 Announce Type: new Abstract: Supervised fine-tuning (SFT) on offline agent trajectories is the standard approach for training specialized tool-using agents, but forcing models to im
arXiv:2610.01162v1 Announce Type: new Abstract: Reliable video world models could provide scalable predictive environments for robot learning, planning, and evaluation. However, generated robot videos
arXiv:2610.00559v1 Announce Type: new Abstract: Vision-Language Models (VLMs) have shown strong multimodal reasoning capabilities, yet whether they truly capture the physical consistency underlying re
arXiv:2610.00929v1 Announce Type: cross Abstract: Models specialized for the same task converge to similar behavior, yet the parameter updates that produce it share no common coordinate system, so wei
Please check out @DeepSeekHarness. We just released packaged desktop versions for macOS and Windows; Linux users can get it from the @deepseek-ai/dsh package on npm. https://deepseek.com/harness Deep
arXiv:2512.05990v2 Announce Type: replace Abstract: Memory consolidation determines both what a learner can do now and which changes remain implementable later. We develop a finite-model synthesis of
arXiv:2610.00314v1 Announce Type: new Abstract: Research agents explain planned experiments. We measure predictive credit with paired forecasts sharing an intervention, forecaster, and outcome while v
arXiv:2610.01383v1 Announce Type: new Abstract: Analogical reasoning involves identifying and preserving relational structures across domains. However, existing approaches to AI-driven multimodal anal
arXiv:2610.00065v1 Announce Type: cross Abstract: Legible planning is the creation of plans that best disambiguate their goals from a set of other candidates from an observer's perspective. In this pa
We benchmarked prompt caching across DeepSeek, GLM, GPT, and Claude using Harbor evals and Phoenix traces, so we could compare cache reuse, estimated cost, and latency on the same multi-turn shopping
arXiv:2610.00354v1 Announce Type: cross Abstract: AI agents that control wallets read attacker-reachable content, so they can be steered into proposing harmful transactions. The usual last line of def
arXiv:2602.23653v3 Announce Type: replace-cross Abstract: Large-scale Vision-Language Models (VLMs) exhibit strong zero-shot recognition, yet their real-world deployment is challenged by distribution
arXiv:2610.01320v1 Announce Type: new Abstract: Generative modeling has shown strong promise for multivariate time mseries (MTS) forecasting, especially scale to high-dimensional settings. Diffusion-b
arXiv:2610.01781v1 Announce Type: new Abstract: Reinforcement learning (RL) for reachability specifications is fundamental to sequential decision-making. Prior work establishes asymptotic convergence
arXiv:2610.00738v1 Announce Type: cross Abstract: The Straight-Through Estimator (STE) is a widely used heuristic for Quantization-Aware Training (QAT), but its surrogate gradients can exhibit substan
arXiv:2610.01554v1 Announce Type: new Abstract: Wanda (Sun et al., 2024) prunes large language models by scoring weights independently within each linear projection, although queries and keys interact
arXiv:2607.17384v3 Announce Type: replace Abstract: This paper provides an experimentally verified formal law for calculating the uplift that diversity of thought provides in Large Language Model (LLM
arXiv:2610.00577v1 Announce Type: cross Abstract: In a district-based election, N voters are partitioned into k districts, and each voter votes for one of m candidates. Each district elects a winner u
arXiv:2610.00204v1 Announce Type: new Abstract: Visual-token compression for vision--language models is posed almost entirely as a selection problem: decide which tokens to keep and discard the rest.
Last month I posted a Qwen3.8-27B LoRA that makes it talk like a person instead of an assistant. It got a lot more attention than I expected: 700+ upvotes, 248 comments and 44k downloads since. I read
arXiv:2610.00700v1 Announce Type: new Abstract: Recent advances in AI for scientific discovery enable molecular understandingand design, yet reasoning over incomplete chemical representations remainsu
arXiv:2610.00541v1 Announce Type: cross Abstract: Recursive models create computational depth through parameter reuse, offering a parameter-efficient alternative to increasing model size. However, mos
arXiv:2610.01344v1 Announce Type: cross Abstract: Reinforcement learning offers the prospect of a reusable sequential decision-making mechanism for spacecraft trajectory design, motivating policy inte
arXiv:2603.26945v2 Announce Type: replace Abstract: Appearance-based gaze estimation (AGE) has achieved remarkable performance in constrained settings, yet we reveal a significant generalization gap w
arXiv:2610.01780v1 Announce Type: new Abstract: A companion that talks with a person for months should come to understand them. It should remember what they said, infer who they are, and know when the
arXiv:2610.01612v1 Announce Type: new Abstract: Continuous legged manipulation requires accurate end-effector tracking while the base keeps walking. Combining reinforcement learning (RL) with model pr
Recursive Language Models: Claude Code, Agent Swarms, Big Research Bets, & the $40M AI Problem Solver https://www.latent.space/p/rlm MIT’s @a1zhang explains why Claude Code, Codex, and Pi are basicall
arXiv:2610.00320v1 Announce Type: cross Abstract: Fine-tuning adapts aligned large language models (LLMs) to downstream tasks, but a few dozen harmful examples can remove their refusal of harmful requ
arXiv:2610.00940v1 Announce Type: cross Abstract: We propose ReHoPER, an inference-only, zero-shot method that improves large language models' reasoning by generating and answering intermediate questi
arXiv:2610.01546v1 Announce Type: cross Abstract: Primal-dual hybrid gradient (PDHG) methods solve large-scale linear programs (LPs) using GPU-friendly matrix-vector products and projections, but thei
arXiv:2610.01728v1 Announce Type: cross Abstract: Understanding loss landscapes is central to explaining neural-network training, yet their structure remains only partially understood even in simple m
arXiv:2610.01222v1 Announce Type: new Abstract: As generative AI (Gen AI) systems take on increasingly autonomous roles in economically and socially consequential interactions, understanding their pro
arXiv:2610.00330v1 Announce Type: cross Abstract: Long-term object search requires learning where objects usually appear from repeated but uneven observations of a changing environment. We formulate r
arXiv:2502.14994v2 Announce Type: replace Abstract: The rapid advancement of AI-generated video poses challenges to digital authenticity and security. Current detection methods, often trained on speci
arXiv:2610.01544v1 Announce Type: new Abstract: Existing image forgery detectors often suffer from generalization to unseen manipulation methods due to the limited ability to capture transferable fore
arXiv:2610.00067v1 Announce Type: new Abstract: Reliable visual inspection is essential for quality assurance in aero-engine blade manufacturing, where defect appearance may vary across production lin
Row-Bot 5.0 is a major rebuild of the local-first AI assistant. The NiceGUI interface is gone, replaced by one React app across desktop, browsers, phones and tablets. The biggest change is how work is
arXiv:2610.02205v1 Announce Type: new Abstract: Programmable world models separate executable dynamics from visual generation, offering a promising foundation for next-generation game engines. However
arXiv:2610.00964v1 Announce Type: cross Abstract: For small merchant businesses (SMBs) whose catalogs fit within a long-context LLM, full-catalog prompting offers a compelling alternative to multi-sta
arXiv:2610.00526v1 Announce Type: cross Abstract: In-context learning (ICL) can be amortized into latent objects (task vectors, function vectors, context vectors) that recover few-shot behavior at zer
arXiv:2610.01908v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) improves vision-language benchmark scores even without visual information during training. With im
arXiv:2609.12822v2 Announce Type: replace Abstract: Blinded physician evaluation has been considered by many to be the gold standard for assessing clinical reasoning in large language models (LLMs). T
arXiv:2610.02202v1 Announce Type: new Abstract: What makes great scientists great? Even as AI systems start to make progress on open problems, scientists remain far ahead of them at sensing which prio
arXiv:2610.00531v1 Announce Type: new Abstract: AI-generated content, often called AI slop, is increasingly common everywhere, particularly in academia. Slop in AI-generated scientific papers, however
arXiv:2610.00084v1 Announce Type: new Abstract: Detailed profession-specific system prompts raise token use and estimated cost per response without a consistent accuracy gain. We evaluate Scientific A
arXiv:2610.01278v1 Announce Type: new Abstract: Alzheimer's disease (AD) diagnosis requires sequential evidence acquisition under heterogeneous test costs and patient burden. Fixed-modality predictors
arXiv:2610.00493v1 Announce Type: new Abstract: Mixture-of-LoRA-experts methods raise the capacity of low-rank adaptation by routing each token to a few low-rank experts. Nearly all of them tie one in
arXiv:2610.00421v1 Announce Type: cross Abstract: Automated classification of brain tumors from MRI is a heavily published application of deep learning in medical imaging, with reported accuracies on
arXiv:2610.00904v1 Announce Type: cross Abstract: Learned manipulation policies rediscover from data the spatial relations that rigid-body mechanics supplies in closed form. This costs data, and it le
arXiv:2602.18181v2 Announce Type: replace Abstract: This work presents SeedFlood, a new approach to decentralized LLM fine-tuning designed to scale across large models, large collaborations, and compl
Hi folks, I'm a long-time lurker and big fan of this subreddit and a massive self-hosting fan (also outside of AI). I doubt many people will disagree with me here because I see the same arguments bein
arXiv:2607.18255v2 Announce Type: replace Abstract: Contribution attribution has become a central problem in LLM-based multi-agent systems, where final outputs are produced through multiple agents, me
arXiv:2610.01046v1 Announce Type: cross Abstract: Collaboration depends on shared context, and technical documentation is one way that context persists across people and AI teammates. Specificity, the
SETA got accepted to NeurIPS 2026! 🎉 We released the full training scripts for DeepSeek-V4-Flash, GLM-5.2 (LoRA), Inkling-Small, and Qwen3.8-27B, with both GRPO and PPO, alongside 4,500+ open-source,
The results are in, no thinking / instruct mode for new models degrade performance more than on old models such as 3.6 vs 3.8, where 3.6 takes the lead on several coding benches in instruct mode. I wo
arXiv:2610.00523v1 Announce Type: new Abstract: Conformal prediction guarantees marginal coverage, but a single calibration threshold can still spread that coverage unevenly, over-covering easy region
arXiv:2610.01180v1 Announce Type: new Abstract: Despite the strong performance of Vision-Language Models (VLMs) on a wide range of visual question answering (VQA) tasks, these models consistently stru
arXiv:2610.02120v1 Announce Type: new Abstract: World action models (WAMs) combine robot action generation with future state prediction. Existing WAMs typically predict videos or learned visual latent