Pai 的 AI 雷达

模型与评测

今天:Advancing price-perf,How NVIDIA Groq。并行还有 Graph Engineering in、Anthropic’s best AI、Your executable is。

日期
2026/08/24
今日条目
40
当前类目
28
#03

Graph Engineering in the Era of LLM Agents: From Individual Intelligence to System Intelligence

LLMs have evolved from language generators to autonomous agents capable of complex, long-horizon tasks. This evolution has produced paradigms including Prompt Engineering to elicit model capabilities, Context Engineering

推荐理由Graph Engineering in the Era of LLM:LLMs have evolved from language generators to autonomous agents capable of complex。
模型与评测HF daily papers
#06

Let's Scale Step by Step: Compute-Efficient Hyperparameter Transfer for Large-Scale Mixture-of-Experts

Mixture-of-Experts (MoE) architectures significantly expand model capacity without a proportional increase in computational cost. However, optimizing their hyperparameters---particularly the learning rate---at extreme sc

推荐理由Let's Scale Step by Step:Mixture-of-Experts (MoE) architectures significantly expand model capacity without。
模型与评测HF daily papers
#12

Every Coin Has Two Sides: On the Dual Nature of Generalization in On-Policy Distillation of Large Language Models

On-policy distillation (OPD) transfers teacher capabilities by supervising trajectories sampled from the student's own policy, yet its generalization behavior remains poorly understood, as most studies evaluate OPD on a

推荐理由Every Coin Has Two Sides: On the Dual:On-policy distillation (OPD) transfers teacher capabilities by supervising trajectories。
模型与评测HF daily papers
#14

AgentMercury: Your Agent Can Synthesize Verifiable Environments for Business Scenarios at scale

Agents learn to act through interaction with environments, yet the environments used for training are often manually constructed or synthesized around predefined tasks and benchmarks. This task-centric paradigm makes it

推荐理由AgentMercury: Your Agent Can Synthesize:Agents learn to act through interaction with environments, yet the environments used。
模型与评测HF daily papers
#17

Partition the Support, Reconstruct the Residual: Training-Free Sparse Attention for Video Generation and World Models

Training-free block-sparse attention can accelerate video transformers, but row-wise attention concentration does not by itself specify an executable sparse operator. Queries sharing a block route may have poorly overlap

推荐理由Partition the Support, Reconstruct:Training-free block-sparse attention can accelerate video transformers, but row-wise。
模型与评测HF daily papers
#22

CLEAR: Continuous Latent Adapter Routing for Utility-Preserving LLM Safety Alignment

Improving the safety of large language models (LLMs) often comes at the expense of utility, as globally applied safety tuning may affect model responses to both harmful and benign inputs. We propose Continuous LatEnt Ada

推荐理由CLEAR: Continuous Latent Adapter:Improving the safety of large language models (LLMs) often comes at the expense。
模型与评测安全治理HF daily papers
#24

Peer-Voted LLM-Agent Stress Tests Find Feed-Induced Lexical Convergence but No Reliable Matched-Exposure Advantage for Distributed Sources

Population-level behavior in large-language-model (LLM) agents cannot be characterized by single-agent benchmarks. We introduce PV-SST, a peer-voted social-platform testbed, and report a separately frozen, preregistered

推荐理由Peer-Voted LLM-Agent Stress Tests Find:Population-level behavior in large-language-model (LLM) agents cannot be characterized。
模型与评测HF daily papers