推論系統

推論 runtime、量化、 serving、KV cache、成本與效能。

最新模型

Rankings Reverse for the Same Models as Token Limits Change, Four-Model Test Records 56,476 Inferences

A new study increased the generation limit for four open-weight models from 64 to 4,096 tokens and found that the leading model changed on all three reasoning benchmarks. Even after truncated outputs were excluded, 3% to 19% of questions changed from correct to incorrect as the budget increased, showing that a single token setting is insufficient for deployment decisions.

推論系統

ThinkRetrieve Retrieves Worked Examples Mid-Reasoning, Boosting Qwen3-1.7B’s AIME 2025 Accuracy by 13.4 Points

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

Muse Glimmer 30B Brings Multimodal Agents to a Single GPU With 17GB Quantized Weights and a DFlash Draft Model

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

Dual-Agent Research System Derives a New Lower Bound for the Grothendieck Constant From Repeated Failures, but the Key Pivot Still Came From Humans

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

ChemWorld Compiles Chemical Processes Into Replayable Agent Environments, Preserving Transaction Receipts Even for Failed Actions

ChemWorld uses typed operations, a compatibility compiler, and private chemical laws to let researchers replace a single world rule while retaining the same public interface. The open-source release passed 52 generated combinations and eight end-to-end workflows, but it validates software execution semantics—not real-world chemical accuracy.

最新模型

UniF-MoE Extracts Shared Blocks Before Routing Residual Computation, Cutting Inference Time by 45.2% Versus Top-2 GMoE

Rather than deciding shared experts, FFN width, and the number of activated experts separately, UniF-MoE lets each token first select shared channels and then accumulates routing probabilities according to its remaining requirements. In the authors’ DeiT and BERT experiments, it improves accuracy and MoE inference cost, but it has not yet been validated on generative large language models or distributed expert parallelism.

最新模型

ReRound Uses a Diffusion Prior to Rechoose Quantization Rounding, Boosting Average Accuracy of 3-/4-Bit Small Models by Up to 1.6 Points

ReRound uses no calibration corpus. Instead, it trains a conditional diffusion model on the model’s own weights to reconsider whether weights near the midpoint of a quantization interval should be rounded up or down. It preserves the existing low-bit format at deployment with no additional inference overhead, but each model requires several hours of model-specific reconstruction upfront.

最新模型

XCoT-VLA Compresses Autonomous-Driving Reasoning into 2–6 Action Tokens, with Interface Latency as Low as 38.6 ms on H100

XPeng introduces XCoT-VLA, replacing dozens of natural-language reasoning tokens with a limited set of semantic action tokens, then using flow matching to generate continuous trajectories. Open-loop tests show lower lateral error, but no closed-loop safety evaluation has been reported, and the latency figures do not cover the full in-vehicle hardware and software stack.

應用研究/AI 基礎設施

GENCO Uses a Single Graph Neural Network for Three Power-Grid Solvers, Boosting Large-Scale AC Power-Flow Throughput by About 29×

Through heterogeneous graph attention, physical-variable decoding, and iterative residual correction, GENCO unifies power flow, optimal power flow, and state estimation. Batched inference on large grids is substantially faster than conventional AC solvers, but its zero-shot cross-grid and real-world SCADA accuracy remains insufficient to directly replace existing tools.