Archive
新模型、開放權重、多模態、語音、影像與模型架構。
最新模型
Liquid AI has released LFM2.5‑VL‑3B, a 3.1B-parameter model that adds screen grounding, layout OCR, multi-image understanding, and function calling. The company reports 228 tokens/s on an Apple M5 Max and roughly 11,000 tokens/s in high-concurrency throughput on a single H100, though the figures have yet to be independently reproduced.
AI 數學與形式驗證
OEIS Open tasks general-purpose language models with proving or disproving 492 integer-sequence conjectures under a fixed budget. The initial SafeVerify evaluation recorded 147 passes, but a second Lean verifier confirmed 144, highlighting how formal benchmarks remain sensitive to differences in specifications and checkers.
代理工具
IBM’s VAKRA puts APIs, document retrieval, and natural-language policies into a single executable trajectory. Even the strongest model achieves only 50% to 70.4% full correctness across different API interfaces. When policies make questions unanswerable, some models’ success rates fall as low as 2.4%.
最新模型
InclusionAI has released the 7.9B-parameter Ling-3.0-tiny, which combines linear attention with sparse MoE and offers BF16, FP8, and INT4 weights. The company says FP8 reaches 86 to 90 tokens per second on an M4 Pro, but vLLM and Ollama support has yet to fully land in stable upstream releases.
代理工具
DashArena evaluates model-generated interactive data dashboards across 234 open-ended tasks and uses a browser to replay the interaction workflows written by the models themselves. Adding interaction evidence raised agreement between an 8B vision judge and human preferences from 71.7% to 79.8%, yet even the best model achieved a full replay rate of only 73.3%.
代理工具
ReTree stores a search agent’s summaries, evidence sources, and revision history as a dependency tree. When new information overturns an earlier premise, the system returns to the node that introduced the error and deletes the affected branches. Results across four search benchmarks show that it both shortens per-step context and improves answers, though hard pruning may discard information that is not actually affected by the conflict.
推論系統
Rather than merely asking models to think longer, ThinkRetrieve retrieves complete solutions to similar problems at each reasoning boundary and inserts them into the next segment of the reasoning context. All five tested models, ranging from 1.5B to 8B parameters, outperformed sequential test-time compute, but the method requires a large, rigorously decontaminated solution database.
最新模型
A Heidelberg University study finds that although individual latents in sparse autoencoders may be interpretable, the full set of activated latents does not map reliably onto human concepts. Simply adding a compatible adjective before a noun can make 20% to 60% of the original activation set disappear, limiting the reliability of interpreting model states through set overlap.
代理工具
Rather than updating the small model’s weights, SKILLER has a stronger model repeatedly revise text-based skills using execution traces and verifier feedback. Qwen3.5-9B’s three-run average on SWE-Skills-Bench rose from 26.2% without skills to 82.8%, although skill generation still relies on GPT-5.4 and successful reference trajectories.
最新模型
Meta has released the Muse Glimmer 30B weights, vision encoder, and DFlash draft model. The 17GB version can run text, image, and speculative decoding workloads simultaneously within 24GB of VRAM. Meta measured an average of 233.4 tokens/s on an RTX 5090, but safety evaluations and deployment details show that the model still requires external safeguards and a newer runtime.
AI 輔助科學研究
Using reasoning models, coding agents, and file-based memory across roughly 240 research sessions, a team ultimately proposed—and the authors verified—`K_G ≥ 6π/11`. The system demonstrated strong local proof capabilities but did not independently recognize that its search had reached a bottleneck; research direction and decisions about which results to pursue still depended on roughly 40 human instructions.
代理工具
Agentic Configuration Management normalizes agents, prompts, models, tools, and workflows into configuration items that can be versioned independently. The reference implementation supports LangGraph, CrewAI, and the OpenAI Agents SDK, but validation currently focuses on small synthetic cases and author-defined oracles.