Search: “reasoning”

arXiv:2609.05579v1 Announce Type: new Abstract: Predicting postoperative spinal morphology from preoperative radiographs could provide valuable support for scoliosis surgical planning, but remains cha

arXiv:2609.06646v1 Announce Type: new Abstract: Annotators routinely disagree on laughter boundaries and subtle chuckles, yet temporal laughter localization typically evaluates against a single refere

You don't need to be a command-line expert to run local AI. http://llama.app wraps the heavy lifting of llama.cpp into a clean UI, making it simple to deploy and manage models like Gemma 4. Instead of

Enterprise agent adoption isn’t one-size-fits-all. While many teams will opt for managed commercial platforms, such as Gemini Enterprise Agent Platform for turnkey agent deployment and governance, dev

arXiv:2609.05113v1 Announce Type: new Abstract: Counterfactual explanations for graph-structured data seek to determine minimal and realistic modifications required in an input graph to alter a model'

arXiv:2609.04641v1 Announce Type: new Abstract: Natural-language-to-SQL systems have ad- vanced rapidly on academic benchmarks, yet production enterprise schemas exhibit graph- like, semi-structured,

arXiv:2609.04343v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used for consequential decisions, making their explanations an important tool for auditing model behavior.

arXiv:2609.05069v1 Announce Type: cross Abstract: Large language models (LLMs) show potential for medical tasks, but their single-turn question-answer format does not reflect how clinical diagnosis is

arXiv:2609.05331v1 Announce Type: new Abstract: Robotic systems increasingly operate in dynamic, uncertain, and open-ended environments, where design-time assumptions may no longer hold, and adaptatio

arXiv:2609.04871v1 Announce Type: new Abstract: Improving an industrial recommender is an iterative research-and-engineering process rather than a direct path from idea to deployment. In extbf{DASHEN,

arXiv:2609.05063v1 Announce Type: cross Abstract: When a customer adds a professional camera to their cart, should the system suggest a matching lens, a generic tripod, or another camera body? Complem

arXiv:2609.05150v1 Announce Type: cross Abstract: This paper introduces Regime-aware Constraint-Based and Noise-Based causal discovery with Markov Blankets (RCBNB-MB), a novel causal discovery algorit

arXiv:2609.04552v1 Announce Type: cross Abstract: Unattended interactive autonomy - machines that step into danger in place of humans and complete tasks with human tools - remains a missing capability

arXiv:2609.04272v1 Announce Type: cross Abstract: This study examines the ability of large language models (LLMs) to predict the risk of weather-related forced outages in the distribution grid in a ze

arXiv:2607.15957v2 Announce Type: replace Abstract: Large Language Models (LLMs) can generate natural language explanations that rationalize their own decisions, a phenomenon commonly referred to as s

Before the introduction, two caveats. This isn't anything as spectacular as the mmh3 stuff filling these subreddits these days, so please go easy on me. It's an experimental tool I'm sharing in its ra

arXiv:2609.04444v1 Announce Type: new Abstract: Benchmarks for the side effects an agent causes on the way to a goal already exist, but HarvestBench is the first to put a price on avoiding the side ef

arXiv:2609.04803v1 Announce Type: new Abstract: Pass receiver selection is a fundamental task in football analytics, aiming to predict the intended receiver under a given game state. This task is chal

arXiv:2609.05037v1 Announce Type: cross Abstract: As LLMs take on roles requiring moral advice, understanding how they attribute moral agency becomes critical. Humans possess moral agency, the capacit

arXiv:2609.05274v1 Announce Type: new Abstract: LLM agents deployed for software engineering fail expensively: they act confidently wrong, and bad actions are recognized only after costly execution an

Jensen Huang says that AGI has been achieved. I strongly disagree. GPT-6 performs really well on benchmarks, including ARC-AGI-3. But benchmark performance is not the same as general intelligence. GPT

arXiv:2609.04564v1 Announce Type: new Abstract: Self-driving laboratories (SDLs) combine automated experimentation with adaptive decision-making to accelerate scientific discovery. Their operation nev

arXiv:2609.05151v1 Announce Type: cross Abstract: Leveraging their inherent sparse event-driven computation, spiking neural networks (SNNs) offer a promising path toward energy-efficient large languag

arXiv:2609.04787v1 Announce Type: new Abstract: Learning-augmented algorithms use fallible predictions while retaining formal performance guarantees. This survey synthesizes prediction interfaces, err

arXiv:2609.04866v1 Announce Type: new Abstract: Recent advances in large language models (LLMs) create opportunities to enrich simulation-based energy policy analysis, particularly by supporting struc

arXiv:2609.04607v1 Announce Type: new Abstract: Three-dimensional scene graphs (3DSGs) have emerged as a promising approach for building geometrically grounded, semantically informed, hierarchical gen

I’ve been setting up an M1 Max Mac Studio (24-core GPU, 32GB unified memory) as a local LLM server and wanted to compare MLX vs llama.cpp on Qwen3.8-27B. I tried to keep the model footprint and benchm

arXiv:2609.04495v1 Announce Type: new Abstract: We formulate indirect prompt injection as a test-time search over a task-dependent attack surface induced by the environment, user task, and injection t

arXiv:2609.04556v1 Announce Type: new Abstract: Runtime traces are becoming a central substrate for understanding agentic systems, yet interpretation has focused largely on what the agent did. Workpla

arXiv:2609.05401v1 Announce Type: cross Abstract: Vision-language models are increasingly used as reward functions for robotic learning, but this role requires paraphrase invariance: the same trajecto

arXiv:2609.04288v1 Announce Type: cross Abstract: Voice AI applications are gaining popularity as advances in large language models (LLMs) enable more natural and accessible spoken interactions. Servi

So, I’m currently just short of being able to run the mainline dense SLMs (Qwen 27b/Gemma 31b), running a i9-14900F/RTX4070 Super/32GB DDR5 setup w/ a 700W 80 Gold PSU and a MSI PRO B760-VC WI-FI IV m

arXiv:2609.05091v1 Announce Type: new Abstract: Industrial anomaly detection must handle two distinct defect families: structural anomalies, which manifest as local texture corruptions, and logical an

arXiv:2609.04735v1 Announce Type: new Abstract: Designing viable drug candidates requires searching a combinatorially large and rugged chemical space for molecules that satisfy multiple, often competi

arXiv:2609.05019v1 Announce Type: new Abstract: Agents tend to optimize, select, or constrain execution structures before decisive runtime outcomes are observed. However, such pre-execution commitment

arXiv:2609.05171v1 Announce Type: new Abstract: Image generation and editing models have advanced rapidly, yet remain unreliable when prompts require external world knowledge. Bounded and long-tail pa

arXiv:2609.04766v1 Announce Type: cross Abstract: Generative AI research has increasingly evaluated factuality, citation, coverage, and report structure. Yet passing such local checks does not by itse

arXiv:2609.04373v1 Announce Type: new Abstract: Large language models (LLMs) are being deployed at scale in consequential real-world systems, from financial markets to content moderation to hiring. We

I'm using DS-V4-Flash-Vision with Q8_K_XL quantization locally as my everyday engine, and for some time now I've been doing a lot of comparisons with Qwen3.8-Flash-Next, also with Q8_K_XL quantization

Hello everyone. I felt current LLM benchmark harnesses hand you headline numbers but offer no tooling to see how models actually answered each question (they dump everything to a JSONL or Parquet file

Jensen has the best gig on the planet. But it is impossible to see how all this is sustainable. Consider the following: Each new model costs vastly more to develop. That’s fabulous for him! On the oth

Emily Forlini / Fortune: OpenAI quietly updates its evaluation metrics for GPT-6 Astra, making changes that appear to favor Astra and continuing to revise other metrics after launch — OpenAI has chang

An agent for every citizen Engineers to upgrade our institutions Robots as a service for all $1 valuation for local individuals & institutions Market rates for everyone else Owns the last mile of inte

I ran a side-by-side ML text-processing and model-training workflow using Fable 5.1 vs. Astra (both on xhigh), and the results could not have been more different. Warning, long post. TL;DR -- Astra co

I just spent the last 2 hours of my life on a Friday night debugging a strange error in a prod CLI app. EF core was receive a readonlyspan during a Contains query. Normally, this query converted to a

GPT-6 is pretty good tbh @viktaur27 @Teslarati The rate of improvement from original GPT to GPT-3 is impressive. If this rate of improvement continues, GPT-5 or 6 could be indistinguishable from the s