Knowledge catalogue
arXiv:2609.02473v1 Announce Type: new Abstract: Ontology rankers remain useful for rare-disease diagnosis because each candidate can be traced to matched patient phenotypes. Large language models (LLM
arXiv:2603.16848v2 Announce Type: replace Abstract: The ``LLM-as-a-judge'' paradigm has become a standard method for evaluating open-ended generation. To address the quadratic scalability costs of pai
arXiv:2608.27463v2 Announce Type: replace Abstract: LLMs now sit on every side of evaluation: as examinees scored on benchmarks, judges of other models' outputs, and raters of human-generated content.
arXiv:2609.02116v1 Announce Type: new Abstract: Reverse-logistics operators often decide how to inspect and route returned assets before their condition is fully observed, while full inspection consum
Side note: when we released ARC-AGI-3 in March, and frontier models scored <1% on it, a few Singularitarian poasters took it as a personal insult, and got very worked up about it. They argued the benc
arXiv:2609.01600v1 Announce Type: cross Abstract: Dynamic agent harnesses let language models change the software that shapes their own execution. This flexibility brings a new reasoning burden: a loc
arXiv:2609.01487v1 Announce Type: cross Abstract: Skill-augmented agents load reusable skills as persistent runtime context, improving task performance but also giving malicious skills a durable chann
arXiv:2609.00982v1 Announce Type: cross Abstract: Using a large language model to play the user is now standard in scalable evaluation. It has a repeatedly diagnosed failure: the simulated user is exc
arXiv:2609.01059v1 Announce Type: new Abstract: As Vision-Language Models (VLMs) tackle dynamic 3D spatial reasoning, ego-motion perception becomes essential to resolve monocular scale ambiguity. Howe
arXiv:2609.01198v1 Announce Type: new Abstract: Repeated banking interactions require assistants to maintain complete, current, and traceable customer records as life changes emerge incidentally in ro
arXiv:2609.01437v1 Announce Type: cross Abstract: As agents move from research prototypes to deployed tools, their capability increasingly depends on model-external execution infrastructure, commonly
arXiv:2609.00015v1 Announce Type: new Abstract: AI agents powered by large language models are evolving from isolated assistants into heterogeneous systems in which multiple agents, planners, controll
arXiv:2603.23934v2 Announce Type: replace-cross Abstract: Large vision-language models (LVLMs) are increasingly being applied to multi-view image inputs captured from diverse viewpoints. Despite this
arXiv:2609.00787v1 Announce Type: new Abstract: Humans need to study only a handful of well-written textbooks to master a discipline and attempt its hardest problems. We argue that an ideal self-evolu
arXiv:2609.01515v1 Announce Type: cross Abstract: Temporal reasoning benchmarks for Video-LLMs are often mediated by language, leaving room for linguistic shortcuts from option wording, answer correla
arXiv:2609.01354v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) and standard benchmark evaluation both rely on an automatic verifier that turns a free text answer
arXiv:2601.12449v2 Announce Type: replace-cross Abstract: AI agents are autonomous systems that combine LLMs with external tools to solve complex tasks. While such tools extend capability, improper to
arXiv:2606.18613v4 Announce Type: replace-cross Abstract: The most plausible near-term role of medical LLMs is to assist rather than replace physicians, yet current evaluations often test isolated cap
arXiv:2508.14323v3 Announce Type: replace Abstract: Tool-augmented LLMs invoke external functions to extend their capabilities, but errors in the invocation decision, such as calling a tool when none
arXiv:2608.30686v1 Announce Type: cross Abstract: Coding agents are increasingly used for software engineering tasks, including bootstrapping projects from third-party repositories whose integrity can
arXiv:2608.30730v1 Announce Type: cross Abstract: Long-horizon agentic tasks go beyond chaining short tasks over more interaction turns. Their evolving dynamic environments and long-range dependencies
arXiv:2608.28982v1 Announce Type: new Abstract: Physics-integrated 3D Gaussian representations make it possible to simulate image-reconstructed assets directly as particles, but current Gaussia-MPM pi
arXiv:2608.30672v1 Announce Type: new Abstract: Recent advances in large language models and multimodal models have pushed remote sensing (RS) processing from simple perception models to agentic syste
arXiv:2608.29919v1 Announce Type: cross Abstract: The rapid proliferation of LLMs has further heightened the need to develop dependable AI-generated text detection, especially beyond English. Neverthe
arXiv:2608.30463v1 Announce Type: new Abstract: Solvability detection is one of the most challenging aspects of mathematical reasoning for Large Language Models (LLMs). While prior work has studied th
arXiv:2608.29609v1 Announce Type: new Abstract: Semantic segmentation is a crucial task for understanding Mars, the most Earth-like planet in our solar system. However, it is challenging because the M
arXiv:2505.03654v3 Announce Type: replace-cross Abstract: Multimodal Large Language Models have shown strong performance across multimodal tasks, and recent personalized MLLMs can recognize user-speci
arXiv:2608.28639v1 Announce Type: new Abstract: Formal theorem proving with large language models remains challenging due to the difficulty of navigating large proof search spaces efficiently. Existin
arXiv:2608.30916v1 Announce Type: new Abstract: Agent evaluations often use one benchmark to choose a workflow and then search for task types where its advantage weakens, so both conclusions are selec
arXiv:2608.29992v1 Announce Type: new Abstract: This paper presents an end-to-end, agent-driven pipeline for the LoD3 reconstruction of facade openings in 3D city models, producing directly usable Cit
arXiv:2603.04191v2 Announce Type: replace Abstract: Large Language Models (LLMs) are increasingly serving as personal assistants, where users may share individual preferences over extended interaction
arXiv:2608.30472v1 Announce Type: new Abstract: Molecular toxicity prediction is increasingly used to prioritise compounds before experimental testing, but conventional benchmark performance can overs
arXiv:2608.29241v1 Announce Type: new Abstract: Clinical voice agents are now deployed in routine care, where real patients do not wait their turn: they interrupt. These systems typically use a cascad
Loop engineering has emerged as a new skill for AI engineers But there is very little research measuring how effective it is. The best results on full tasks in a new benchmark is ~25%. LoopArena from
arXiv:2608.27879v1 Announce Type: new Abstract: Articulated human pose provides detailed body-configuration information beyond coarse spatial relationships, but whether this detail yields greater disc
I ran a few benchmarks on my MacBook Pro M5 Max with oMLX: Running Qwen3.8-27B with thinking off has a big effect on output quality. Running it with thinking on xhigh burns 5.5x the tokens and runs 6x
Below are the benchmark results for running Qwen3.8-Flash-Next on Strix Halo using the Vulkan backend of llama.cpp, combined with MTP model. Hardware Item Details CPU AMD Ryzen AI MAX+ 395 (16C/32T) G
arXiv:2608.26807v1 Announce Type: cross Abstract: Travel planning agents assist users in generating personalized travel plans by modeling their individual preferences. Existing agents either rely on e
arXiv:2608.26131v1 Announce Type: new Abstract: As Large Language Models (LLMs) are increasingly deployed to serve open-ended, multi-turn interactions, evaluating conversational quality at human scale
arXiv:2608.26157v1 Announce Type: new Abstract: Natural-language analytics over enterprise data warehouses is increasingly important, but production use is limited by hallucinated metrics, invalid joi
arXiv:2608.26237v1 Announce Type: cross Abstract: Capture-the-Flag (CTF) benchmarks are widely used to assess the offensive security capabilities of autonomous language-model agents. Evaluations rely
arXiv:2608.27345v1 Announce Type: cross Abstract: Recent video generation models are increasingly framed as world models. Many physical processes can unfold in more than one valid way. Therefore, a wo
arXiv:2608.27154v1 Announce Type: new Abstract: Vision Language Models (VLMs) have shown great success in general visual tasks, yet they still struggle to deeply understand text within images. In this
arXiv:2606.07867v2 Announce Type: replace Abstract: Are tool-calling LLM agents equally safe throughout a conversation? We discover they are not: agents are most vulnerable at the very start of a sess
arXiv:2608.24903v1 Announce Type: cross Abstract: Mobile and wearable sensing enables longitudinal observation of behavior, yet translating these signals into meaningful mental health constructs remai
arXiv:2608.25158v1 Announce Type: cross Abstract: Evaluating the ability of large language models (LLMs) to discover software bugs is increasingly important. Existing benchmarks typically evaluate thi
arXiv:2608.25529v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) have shown strong performance in video understanding. However, their ability to follow instructions in this dom
arXiv:2608.24039v1 Announce Type: cross Abstract: Manufacturing process planning transforms heterogeneous design information into coherent manufacturing decisions. However, existing approaches focus o
arXiv:2608.23666v1 Announce Type: new Abstract: Sycophancy and hallucination are persistent failure modes of Large Language Models (LLMs) across domains. However, it becomes particularly consequential
arXiv:2608.24160v1 Announce Type: new Abstract: Multimodal understanding models that can jointly judge text-to-image (T2I), text-to-video (T2V) and text-to-speech (TTS) generation are increasingly use
arXiv:2602.00704v2 Announce Type: replace Abstract: The generation of Register-Transfer Level (RTL) code is a crucial yet labor-intensive step in digital hardware design, traditionally requiring engin
arXiv:2608.22510v1 Announce Type: new Abstract: Agent benchmarks often evaluate only final answers even when agents run on stateful runtimes. We argue this under-specifies what is being evaluated: the
arXiv:2608.21382v1 Announce Type: new Abstract: Multiple-choice benchmarks fix the questions and the correct answers, but not the harness: the order of the options, the wording of the prompt, and whet
arXiv:2608.20587v1 Announce Type: cross Abstract: We describe the winning entry to the MoCha 2026 Benchmark and Challenge on Parkinsonian Gait, which predicts MDS-UPDRS gait severity from canonicalize
arXiv:2608.20797v1 Announce Type: new Abstract: Evaluating language-guided mobile agents has recently shifted from rule-based to model-based approaches to achieve scalable and automated assessments. H
arXiv:2608.21209v1 Announce Type: new Abstract: The rise of agentic AI enables LLMs to access diverse user data, raising critical privacy concerns. Prior work on contextual privacy studies whether LLM
arXiv:2608.20656v1 Announce Type: cross Abstract: Traffic sensors commonly record flow, speed, and occupancy, but standard traffic flow forecasting benchmarks and models rarely exploit all three raw m
arXiv:2601.09487v2 Announce Type: replace Abstract: The rapid evolution of Large Language Models (LLMs) has fostered diverse paradigms for automated slide generation, ranging from code-driven layouts
Hi all Looking at a PowerColor R9700 for Qwen3.8-27B, Q4, llama.cpp/Vulkan. AMD's own blog quotes 51.8 tok/s but doesn't say what context length that's at, or whether MTP=2 was holding up. Separately
Most YouTube benchmarks only show trivial tasks like generating landing pages or simple Three.js games. Is a local model like Qwen 3.8 27B actually capable of real-world systems programming—such as bu