Knowledge catalogue
arXiv:2601.16312v2 Announce Type: replace-cross Abstract: Research in AI4Science has shown promise in many science applications, including polymer design. However, current LLMs are ineffective in this
arXiv:2605.08019v1 Announce Type: new Abstract: Humans rapidly learn abstract knowledge when encountering novel environments and flexibly deploy this knowledge to guide efficient and intelligent actio
arXiv:2605.04040v1 Announce Type: new Abstract: Text-to-image generation has advanced rapidly with diffusion models, progressing from CLIP and T5 conditioning to unified systems where a single LLM bac
arXiv:2605.02819v1 Announce Type: new Abstract: Large language models excel at complex reasoning, yet evaluating their intermediate steps remains challenging. Although process reward models provide st
arXiv:2605.01474v1 Announce Type: new Abstract: Predicting future clinical outcomes from electronic health records (EHR) remains challenging due to the complexity and heterogeneity of patient data. LL
arXiv:2604.20705v1 Announce Type: new Abstract: Reinforcement learning (RL) with verifiable rewards (RLVR) has demonstrated the great potential of enhancing the reasoning abilities in multimodal large
arXiv:2604.19689v1 Announce Type: new Abstract: Understanding artworks requires multi-step reasoning over visual content and cultural, historical, and stylistic context. While recent multimodal large
arXiv:2604.19395v1 Announce Type: new Abstract: While self-consistency is known to improve performance on symbolic reasoning, its effect on the recall of encyclopedic knowledge is unclear due to a lac
arXiv:2604.17290v1 Announce Type: new Abstract: LLMs are widely used for code generation and mathematical reasoning tasks where they are required to generate structured output. They either need to rea
arXiv:2604.15859v1 Announce Type: cross Abstract: Forecasting has become a natural benchmark for reasoning under uncertainty. Yet existing evaluations of large language models remain limited to judgme
arXiv:2603.27064v2 Announce Type: replace-cross Abstract: Understanding charts requires models to jointly reason over geometric visual patterns, structured numerical data, and natural language -- a ca
arXiv:2601.19917v2 Announce Type: replace Abstract: Strategic planning is critical for multi-step reasoning, yet compact Large Language Models (LLMs) often lack the capacity to formulate global strate
arXiv:2604.03401v2 Announce Type: replace-cross Abstract: Understanding student engagement usually requires time-consuming manual observation or invasive recording that raises privacy concerns. We pre
arXiv:2604.09494v1 Announce Type: cross Abstract: We propose RecaLLM, a set of reasoning language models post-trained to make effective use of long-context information. In-context retrieval, which ide
arXiv:2505.15960v3 Announce Type: replace Abstract: Process Reward Models (PRMs) have emerged as a promising approach for improving LLM reasoning capabilities by providing process supervision over rea
arXiv:2512.23365v3 Announce Type: replace Abstract: The rapid progress of Multimodal Large Language Models (MLLMs) has unlocked the potential for enhanced 3D scene understanding and spatial reasoning.
arXiv:2610.17593v1 Announce Type: cross Abstract: Language provides a natural interface for humans to specify and reason about goals, but training language-conditioned robotic policies and reasoning t
arXiv:2610.12522v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used in molecular discovery and structure elucidation, where decisions depend on physical observations.
arXiv:2610.13238v1 Announce Type: new Abstract: On-Policy Distillation (OPD) has emerged as a powerful paradigm for transferring reasoning capabilities from frontier models to smaller models, while ex
As i use a rather tiny setup 12gb vram gpu and 32bg ram, i was wondering: What setup do you use, which harness? and how do you customize it to run qwen 3.8 flash next swift with a high context length
arXiv:2610.07981v1 Announce Type: new Abstract: Higher-order models (e.g., hypergraph neural networks) often outperform lower-order baselines on hypergraph learning benchmarks, and their advantages ar
arXiv:2609.39953v1 Announce Type: new Abstract: Omni-modal large language models (OmniLLMs) enable unified audio-video understanding, but their long multimodal token sequences make deployment computat
arXiv:2609.35539v1 Announce Type: new Abstract: Video segmentation models maintain object identities by carrying instance information across frames. Under prolonged occlusion, reappearance, or interac
arXiv:2609.25244v2 Announce Type: replace-cross Abstract: Children increasingly interact with AI chatbots, making trust calibration essential to AI literacy. Prior research has examined children's tru
arXiv:2609.27621v1 Announce Type: new Abstract: Physical modeling and inverse design require computation that can continue from reusable state. We introduce SHRAV, an architecture-independent computat
arXiv:2609.24760v1 Announce Type: new Abstract: When facing complex problems, humans tend to try various ideas for different issues. Human thinking patterns exhibit remarkable flexibility in adapting
arXiv:2609.21323v1 Announce Type: new Abstract: Vision-language models (VLMs) have demonstrated strong scene understanding and semantic judgment across diverse tasks, but their appropriate role in coo
Screenshot of heavy load. I did offload vision to a spare 2060 instead of local ram. The control panel was generated with qwen3.8-27b davidau q4k_m max mtp. Made the panel from scratch over a week, ba
arXiv:2609.19006v1 Announce Type: cross Abstract: Large Language Models (LLMs) and Large Reasoning Models (LRMs) are typically evaluated on challenging benchmarks through dataset accuracy alone, provi
arXiv:2609.16233v1 Announce Type: cross Abstract: Vision-language models excel at 2D image understanding but remain limited in 3D spatial reasoning. Progress is hindered by limitations in current benc
arXiv:2609.14018v1 Announce Type: new Abstract: A correct diagnosis can still be reached for the wrong reasons. In clinical reasoning, every disease hypothesis creates obligations: key evidence must b
arXiv:2609.10008v1 Announce Type: new Abstract: Composed video retrieval (CoVR) searches a gallery for the target video that realizes a natural-language modification of a source clip. However, at gall
arXiv:2608.30156v1 Announce Type: new Abstract: Plane geometry problem (PGP) solving has become a critical benchmark for multimodal reasoning because it requires accurate visual perception and precise
arXiv:2608.29374v1 Announce Type: new Abstract: Tool-augmented multimodal reasoning integrates external tools (e.g., object detection, depth estimation) into multimodal large language models (MLLMs) t
arXiv:2601.08058v2 Announce Type: replace-cross Abstract: Chain-of-Thought (CoT) prompting often improves the reasoning performance of large language models (LLMs), but the internal signal that trigge
arXiv:2608.31068v1 Announce Type: new Abstract: When a large language model fails a reasoning task, it is often assumed to lack the underlying capability. However, this conflates a genuine absence of
## Qwen3.8-Flash-Next-NVFP4 (inferact) vs Qwen3.8-27B-FP8 (qwen) Slammed with work and no time to pretty this up. Qwen wrote most of this but I checked the data. All tests done on the same rig, same p
arXiv:2608.27455v1 Announce Type: new Abstract: Recent advances in inference-time scaling have significantly improved the reasoning performance of large language models (LLMs). However, these methods
arXiv:2603.16313v3 Announce Type: replace Abstract: Electronic control units (ECUs) embedded within modern vehicles generate a large number of asynchronous events known as diagnostic trouble codes (DT
arXiv:2608.26070v1 Announce Type: new Abstract: Test-time scaling uses extra test-time compute to improve performance, such as letting language models reason longer when solving a problem. As models k
arXiv:2608.24319v1 Announce Type: new Abstract: An intelligent system does not merely reason: it governs its own reasoning - how much to compute, when to stop, which module to activate. Can that role
arXiv:2505.24255v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have shown potential in simulating human behaviors and performing theory-of-mind (ToM) reasoning, crucial for com
arXiv:2608.19583v1 Announce Type: cross Abstract: Recent studies suggest that video generation models can exhibit certain forms of zero-shot visual reasoning through generated frames. Yet reliable eva
arXiv:2608.15539v1 Announce Type: new Abstract: Video understanding benchmarks have long centered on single-camera settings, where modern multi-modal language models achieve strong performance across
arXiv:2608.16082v1 Announce Type: new Abstract: Infrared (IR) spectra provide characteristic signals of molecular structure, which are often interpreted by experts via functional-group identification
arXiv:2608.13267v1 Announce Type: cross Abstract: Existing vision-language model (VLM) benchmarks emphasize perception and reasoning accuracy (how well VLMs describe and reason about what they see in
arXiv:2608.12348v1 Announce Type: cross Abstract: Streaming systems increasingly hand work to large language models (LLMs) -- writing pipelines, triaging alerts, reading logs -- and all of it assumes
arXiv:2608.08113v1 Announce Type: new Abstract: Chain-of-Thought (CoT) prompting has become the dominant paradigm for eliciting reasoning in Large Language Models (LLMs), yet it creates substantial co
arXiv:2602.06337v2 Announce Type: replace-cross Abstract: Causal inference is essential for decision-making but remains challenging for non-experts. While large language models (LLMs) show promise in
arXiv:2607.09786v3 Announce Type: replace-cross Abstract: To curb overthinking and reduce inference costs, researchers now train reasoning models with penalties on chain of thought length. We find tha
arXiv:2607.22872v1 Announce Type: cross Abstract: Post-training quantization (PTQ) has become a practical solution for deploying deep learning models on resource-constrained edge devices by compressin
arXiv:2607.13069v1 Announce Type: new Abstract: Large language models produce chain-of-thought (CoT) reasoning that appears logically sound yet may not genuinely depend on its stated premises. We intr
Introduction RxBrain (Hy-Embodied-RxBrain-1.0) is a unified multimodal foundation model for embodied cognition — a single model that couples language reasoning with visual imagination to deliver three
arXiv:2607.07405v1 Announce Type: new Abstract: Tool-using LLM agents can violate the very policies they are deployed to enforce while appearing to complete the task successfully. In policy-permissive
arXiv:2607.05168v1 Announce Type: new Abstract: Why do intelligent systems need to perform explicit symbolic reasoning? Computer science has traditionally regarded symbolic reasoning as a defining com
arXiv:2602.20094v2 Announce Type: replace Abstract: As large language models (LLMs) witness increasing deployment in complex, high-stakes decision-making scenarios, it becomes imperative to ground the
arXiv:2606.27721v1 Announce Type: new Abstract: Compositional generalization, the ability to solve complex problems by combining solutions to simpler sub-problems, is a fundamental capability of both
Gary Marcus argues that large language models based on transformers quickly reach their limitations, prompting developers to add external tools as workarounds, while neurosymbolic AI—which combines ne
arXiv:2606.06679v1 Announce Type: cross Abstract: Court judgments are central to legal practice and jurisprudence, yet discourse analysis of Hong Kong judgments has received limited attention, owing l
arXiv:2606.05983v1 Announce Type: cross Abstract: Generative AI makes answers easy and understanding hard, and uncritical use invites cognitive offloading. Schools still measure unaided performance, y