Search: “reasoning”

SPCX isn't just building compute faster than every other neocloud It has something none of them have: OPTIONALITY on every GPU it deploys Every GW SpaceX brings online has two paths to revenue: 1. use

arXiv:2610.14626v1 Announce Type: new Abstract: Mixed-variable optimization in scientific discovery often requires exploring large combinatorial spaces under costly experimental evaluations, making sa

arXiv:2610.14440v1 Announce Type: new Abstract: Asynchronous reinforcement-learning post-training of large language models is computationally efficient but inherently off-policy: the learner updates o

arXiv:2610.18256v1 Announce Type: new Abstract: On-policy self-distillation (OPSD) has emerged as a popular training paradigm for LLMs, providing dense, token-level supervision by aligning a model's o

arXiv:2610.16977v1 Announce Type: new Abstract: Visual in-context learning (ICL) enables vision-language models (VLMs) to adapt at test time via demonstration examples, but its effectiveness depends c

arXiv:2610.14923v1 Announce Type: new Abstract: The TLA+ (temporal logic of actions) formal specification toolkit has seen widespread adoption in modeling modern distributed software systems including

arXiv:2610.17657v1 Announce Type: new Abstract: An embodied brain, or System 2, must turn an abstract goal into actions while discovering relevant scene conditions and checking whether executed skills

I've seen this one floating around quite a bit now and since this is something that can be done without the parent and or other dependencies i thought i'd share something. Usually when you fine-tune a

arXiv:2610.15038v1 Announce Type: new Abstract: Real-world Vision-Language Navigation (VLN) requires agents to act under partial observability, where map structure, semantic cues, and target locations

arXiv:2610.14077v1 Announce Type: cross Abstract: Embodied Audio-Visual Navigation (AVN) requires an agent to locate sound targets by jointly leveraging visual and auditory cues. While prior work has

[UPDATE / RETRACTION: The '100% GTO' claim had a prompt-leakage bug] We patched the repo to pass strictly neutral states, here are the real, honest numbers: • Latency: Still ~2ms (ONNX + Absorbing Mar

arXiv:2610.15888v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards spends much of its rollout budget on states that yield no learning signal. We recast this as a question a

arXiv:2610.14000v1 Announce Type: new Abstract: Group-relative reinforcement learning methods such as GRPO train large language model (LLM) agents by comparing rollouts for the same task. When every t

arXiv:2610.18615v1 Announce Type: new Abstract: Visual agents must turn video observations into timely decisions to track progress and choose what to do next. Building on the emerging System One parad

arXiv:2610.17902v1 Announce Type: new Abstract: Visual question answering models often produce plausible responses even when the available visual evidence is insufficient to support a valid answer. Al

arXiv:2610.14731v1 Announce Type: new Abstract: Long-term memory benchmarks for language agents primarily evaluate whether information from earlier interactions can be retrieved and used when relevant

arXiv:2610.16020v1 Announce Type: new Abstract: Layer-wise post-training quantization methods let each layer compensate for the quantization error inherited from earlier layers. We ask under what assu

arXiv:2610.17791v1 Announce Type: new Abstract: Understanding facial behaviour in video requires answering three fundamental questions: when a key facial expression occurs, where the model attends on

arXiv:2610.14530v1 Announce Type: new Abstract: Multi-robot systems (MRS) offer significant advantages over single-robot platforms by enabling information sharing and coordination for different tasks

arXiv:2610.15394v1 Announce Type: new Abstract: Structured pruning is typically formulated as identifying which model dimensions to remove, often within the model's original representation. However, t

tests : consolidate context state tests into test-llama-context (#30023) tests : consolidate the context state tests into test-llama-context test-save-load-state, test-recurrent-state-rollback and tes

It's confirmed. OpenAI has been secretly replacing Astra with increasingly shit models since release. My evidence is overwhelming. Three Reddit comments, a benchmark that fluctuated 4%, and a guy who

After months sitting on the sidelines of the frontier AI race, Google has finally launched its long-awaited Gemini 4 Argon model, claiming it can compete with the best the industry has to offer. Googl

A lot of projects have come up recently consisting of specific hardware config specialized forks of existing conferencing engines, but they all seem to be made using Claude or OpenAI's biggest frontie

If an agent saves an engineer 2 hours to implement a feature but collectively costs the team 3 hours in alignment and review, the ROI is negative. Isolated chats are suboptimal in team environments be

A coding agent finishes a change and wants to open a GitHub issue with a link to its results. The agent can describe the issue, but creating it belongs to a different system. It needs a tool that can

Lipwise is a [yet another] dictation app for Windows, macOS and Linux. You hold a hotkey, talk, and it types into whatever app has focus but with a twist. Since most of us that speak to our computers

When we talk about agents, that doesn't necessarily mean we're talking about Python. In today's modern ecosystem, an agent might run as a high-concurrency Go microservice on Cloud Run, or live inside

Cybersecurity company Trellix today launched Trellix Wise for Private Cloud to let enterprises run its artificial intelligence engine for security operations entirely inside their own environments. Th

arXiv:2610.13354v1 Announce Type: new Abstract: The NIST Assembly Task Board #1 remains a challenging benchmark for learning-based methods in robotic manipulation. Over short horizons, it demands prec

arXiv:2603.07427v3 Announce Type: replace Abstract: As Large Language Models (LLMs) evolve into autonomous agents, existing safety evaluations face a fundamental trade-off: manual benchmarks are costl

arXiv:2610.13277v1 Announce Type: cross Abstract: Memory-augmented assistants retrieve evidence from stored conversations and documents to answer queries. Most embedding-based retrievers compare singl

arXiv:2610.13481v1 Announce Type: new Abstract: On-policy distillation (OPD) provides teacher supervision on student-generated trajectories, but a single trajectory realizes only one continuation at e

arXiv:2610.12711v1 Announce Type: new Abstract: Federated Large Language Models (FedLLMs) are promising to deliver privacy-preserving, collaborative intelligence. The decentralized training process is

arXiv:2610.12655v1 Announce Type: new Abstract: LLM agents can execute increasingly complex tasks, but effective assistance also requires aligning their actions with goals and constraints that users m

arXiv:2602.03806v2 Announce Type: replace-cross Abstract: Training large language models (LLMs) for multi-turn code generation typically relies on online reinforcement learning (RL), in which models l

arXiv:2610.12570v1 Announce Type: new Abstract: Speculative decoding accelerates large language model inference by using a lightweight drafter to propose multiple tokens that are subsequently verified

arXiv:2610.13264v1 Announce Type: cross Abstract: On-policy distillation (OPD) reveals versatility and convenience during VLM post-training, providing dense supervision for student from teacher. Howev

arXiv:2610.13536v1 Announce Type: cross Abstract: When a language model is distilled from a teacher that shares its base parameters, the student can inherit behavioural traits of the teacher---includi

arXiv:2610.12758v1 Announce Type: new Abstract: Language models have been adopted faster than evaluation practices that reliably assess their risks, and a growing share of use is affective rather than

arXiv:2610.13287v1 Announce Type: new Abstract: Prompt optimizers rewrite a prompt to raise one target metric, but prompts also state requirements the metric does not score, such as an output format,

arXiv:2610.13807v1 Announce Type: new Abstract: Data augmentation is standard practice for low-resource automatic speech recognition (ASR), but its benefits are almost always reported as aggregate err

arXiv:2610.12998v1 Announce Type: new Abstract: Creativity benchmarks for large language models (LLMs) vary widely in task and metric design. This raises the question of whether they capture a common

arXiv:2610.06411v2 Announce Type: replace Abstract: Agentic systems increasingly coordinate molecular-design tools, but it is unclear which layer of the stack limits outcomes on real projects. We deve

arXiv:2610.12669v1 Announce Type: cross Abstract: Large language models are becoming central to GeoAI systems, yet their evaluation rarely considers the elicitation of geospatial data requirements. Th

arXiv:2610.13035v1 Announce Type: cross Abstract: Multi-view representations enable pretrained image and video vision-language models to understand 3D scenes, but their long visual token sequences mak