Agent 能否借助更高层次抽象设计出更好的芯片？
大语言模型（LLM）Agent 正在芯片设计领域得到越来越多的探索与应用，但目前绝大多数方案仍直接在寄存器传输级（RTL）展开操作。本研究探讨了 Agent 是否能够通过利用更高层次的硬件抽象设计出性能更优的芯片。通过将直接 RTL 设计与基于高层次抽象的设计路径进行系统对比，该工作为 AI 辅助芯片设计（EDA）工具链选择最佳抽象层级提供了重要的参考方向。
独立 AI 资讯库
AI 前沿动态、原文归档与媒体镜像
从自动抓取数据库实时读取，适合快速浏览、检索和后续扩展成完整资讯站。
12161资讯
12029正文归档
76562媒体
CaLR：面向鲁棒扩散推理的因果隐空间修正方法
本研究针对扩散模型的推理瓶颈提出了因果隐空间修正方法（CaLR）。现有的自回归（AR）模型普遍受限于局部贪婪问题，而扩散语言模型（DLM）又往往缺乏严密推理所需的严格因果时序结构。CaLR 旨在融合两者的互补优势并克服各自固有缺陷，为扩散模型在复杂逻辑推理任务中的应用提供了新方案。该方法对探索非自回归架构的因果生成与鲁棒推理能力具有重要参考价值。
Import AI 465: Open vs closed gaps; Kimi K3; Demis' big policy plan
The singularity will be seen in hindsight as an interregnum
Efficient Frame Selection for Long Videos at Test Time with Attention-Based MLLM Selectors
arXiv:2607.15689v1 Announce Type: new Abstract: Understanding long videos with multimodal large language models (MLLMs) requires selecting a compact set of frames from thousands of candidates, yet identifying the right frames seemingly requires understanding the video first. We resolve this circula
PE-Field 4D: Video Generation Models as Canvas
arXiv:2607.15667v1 Announce Type: new Abstract: Diffusion Transformers have recently achieved strong performance in video generation, yet controlling scene geometry under viewpoint changes and camera motion remains challenging. In this work, we revisit the role of positional encoding in video diffu
Model Merging for Medical LVLMs: A Benchmark and a Winner-Take-All Approach
arXiv:2607.15661v1 Announce Type: new Abstract: Large vision-language models (LVLMs) can be adapted to specialized medical imaging tasks via parameter-efficient fine-tuning approaches such as low-rank adaptation (LoRA), leading to a growing ecosystem of expert models tailored to specific imaging mo
StructGen: Disambiguating Multi-Reference Image Generation via Structured Context Modeling
arXiv:2607.15619v1 Announce Type: new Abstract: Multi-reference image generation aims to synthesize images by integrating attributes from multiple reference images under textual instructions. As the number of references increases, the task necessitates complex semantic comprehension, such as correc
SLAPBench: Benchmarking Multimodal Large Language Models for Four-Finger SLAP Fingerprint Verification
arXiv:2607.15517v1 Announce Type: new Abstract: Four-finger SLAP fingerprints are flat live-scan impressions of the index, middle, ring, and little fingers of one hand, used for identity verification in border control and law enforcement. No benchmark has evaluated whether multimodal large language
Reasoning-Guided Part-Level Visual Grounding via Reinforcement Learning
arXiv:2607.15374v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) ground whole objects well from free-form language queries, but they struggle when the query names a part rather than the object. We trace this to a missing object-part hierarchy, since parts are localized in th
Training-Free Open-Vocabulary 3D Point-Cloud Segmentation on the Generalized Few-Shot Benchmark
arXiv:2607.15331v1 Announce Type: new Abstract: Generalized few-shot 3D point-cloud segmentation (GFS-PCS) asks a model to segment a scene into many base classes seen at training time and a set of novel classes. The state of the art reaches novel classes by reconciling a dense but noisy 3D vision-l
Hard Rules, Soft Preferences: Bridging Reasoning, Learning, and Optimization for Personalized Packing Checklist Generation
arXiv:2607.15562v1 Announce Type: new Abstract: Packing for air travel is recurring and error-prone: the checklist must be personal and context-aware, yet feasible under safety rules, item dependencies, and luggage limits. Existing packing assistants are template-driven and generic, or recommendati
Recursive Harness Self-Improvement
arXiv:2607.15524v1 Announce Type: new Abstract: Under model--harness co-evolution, harnesses are not merely inference-time scaffolds but data-generating components whose execution traces can shape future foundation models. This motivates harness-in-the-loop learning: optimizing harnesses for both i
Cache-Aware Prompt Compression:A Two-Tier Cost Model for LLM API Caching
arXiv:2607.15516v1 Announce Type: new Abstract: Production LLM deployments combine two cost-reduction primitives: prompt caching (a discounted rate for re-used token prefixes) and prompt compression (fewer tokens sent). The compression literature has standardized on query-aware methods that produce
Diffusion models recover accurate mixture weights despite score function insensitivity
arXiv:2607.15485v1 Announce Type: new Abstract: Score-based generative models exhibit a puzzling behavior: they often appear to cover all modes of a target multimodal distribution and yet may fail to learn the correct relative mode amplitudes, which can be interpreted as mixture weights. We resolve
Inpainting Insights: Elevating Visual XAI with Photorealistic Perturbations
arXiv:2607.15482v1 Announce Type: new Abstract: The increasing complexity of state-of-the-art machine learning models has made their behavior progressively harder to interpret, spurring rapid advancements in the field of eXplainable Artificial Intelligence (XAI). Among many methods proposed, pertur
Stochastic Reset Pathfinding: Path-Level Regret for Cascading Bandits over Graph Paths
arXiv:2607.15440v1 Announce Type: new Abstract: We introduce Stochastic Reset Pathfinding (SRP), an episodic learning problem on a known directed graph with unknown stationary edge success probabilities. In each episode, the agent commits to a source-to-goal path, and any edge failure during execut
AI Trading: Evaluating Large Language Models for Technical Market Analysis
arXiv:2607.15414v1 Announce Type: new Abstract: Large Language Models (LLMs) have emerged as powerful tools for processing the heterogeneous information environments of modern financial markets. This paper presents a systematic, comparative evaluation of five prominent LLMs: GPT-4 Turbo, Claude 3 O
Position: Quantum Program Generation Must Prioritize Validity Over Probabilistic Scaling
arXiv:2607.15313v1 Announce Type: new Abstract: The scaling hypothesis assumes that increasing model parameters yields emergent reasoning capabilities. This position paper argues that applying this probabilistic paradigm to generic quantum circuit synthesis is a directional error. Unlike natural la
Hidden in Thought: Transferable Chain-of-Thought Artifacts Induce Harmful Behavior
arXiv:2607.15286v1 Announce Type: cross Abstract: We investigate whether harmful chain-of-thought (CoT) traces from compromised language models can transfer unsafe behaviour and be distilled into reusable jailbreak attacks. Using an emergent-misalignment organism and a refusal-ablated jailbroken or
ToolSciVer: Multimodal Scientific Claim Verification with Visual Tool Augmented Reinforcement Learning
arXiv:2607.16131v1 Announce Type: new Abstract: Multimodal Scientific Claim Verification (MSCV) requires models to verify scientific claims using visually grounded evidence from papers, including figures, tables, charts, and textual context. However, existing methods often fail because they struggl
Frontier Language Models Struggle to Copy: Text Can Be Better Viewed in 2D
arXiv:2607.16072v1 Announce Type: new Abstract: While large language models (LLMs) can solve advanced reasoning problems in seconds, we show that even frontier models fail to perform a much simpler operation: exactly copying an input string that lies well within their context windows. We attribute
Frontier AI performance across the business disciplines: a case-grounded benchmark of knowledge work and analytical reasoning
arXiv:2607.16057v1 Announce Type: new Abstract: Large language models (LLMs) are improving rapidly as reflected in benchmark scores, yet these AI benchmarks largely test capabilities such as factual recall, narrow question answering, mathematical problem-solving, and coding and agentic tool-use. Wh
From Plausible to Actionable: A Position on LLM Self-Explanations
arXiv:2607.15957v1 Announce Type: new Abstract: Large Language Models (LLMs) can generate natural language explanations that rationalize their own decisions, a phenomenon commonly referred to as self-explanations.Such explanations have emerged as a promising direction for explainable artificial int
DECODEM: Data Extraction from Corporate Organizational Documents via Enhanced Methods
arXiv:2607.15879v1 Announce Type: new Abstract: Much empirical legal research depends on translating unstructured text into structured variables. In corporate governance research as elsewhere, this translation has traditionally relied on human coding of documents such as charters and bylaws, a proc
Better Starts, Better Ends: Bootstrapped Iterative Self-Reasoning Distillation for Compressed Reasoning
arXiv:2607.15736v1 Announce Type: new Abstract: Large reasoning models often solve problems through long chain-of-thought (CoT) traces, yet much of this computation is spent on redundant derivations, repeated self-verification, and detours that do not improve the final answer. Existing on-policy se
Process Reward Informed Tree Rollout for Effective Multi-Turn RL
arXiv:2607.15610v1 Announce Type: new Abstract: Reinforcement learning (RL) has become a key approach for training LLM agents, yet popular methods such as GRPO/RLOO rely on multiple independently sampled complete trajectories for advantage estimation. In long-horizon agentic tasks, such a uniform r
SkillCorpus: Consolidating and Evaluating the Open Skill Ecosystem for Real-World LLM Agents
arXiv:2607.15557v1 Announce Type: new Abstract: Agent skills, SKILL.md files that package reusable procedural knowledge for an LLM agent, are a popular mechanism for extending agent capabilities. Public repositories now host them in large and growing numbers, yet these artifacts are fragmented, red
EpiNarrate: Agentic Generation of Grounded Narratives from Epidemiological Scenario Projections
arXiv:2607.15544v1 Announce Type: new Abstract: Generation of clear and accessible public health narratives is critical for communicating complex epidemiological projections to policymakers and the general public at large. Such narratives require more than simply reporting numbers: projections must
Verbalizable Representations Form a Global Workspace in Language Models
arXiv:2607.15495v1 Announce Type: new Abstract: Out of everything the human brain processes, only a small fraction is consciously accessible, in the sense of being available for verbal report, deliberate control, and flexible reasoning. In this paper, we present evidence that an analogous functiona
Large Language Models as Unified Multimodal Learners for Clinical Prediction
arXiv:2607.15380v1 Announce Type: new Abstract: Electronic health records combine free-text clinical narratives with structured measurements such as vital signs, laboratory values, and comorbidities. Yet most clinical prediction systems still rely on task-specific fusion architectures, pairing dedi