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1536 technical news articles

AeroCopilotBench Turns Flight Procedures Into Executable State Machines, Yet the Best Agent’s Safe Pass Rate Is Still Only 72.6%

The new benchmark uses 73 abnormal cockpit tasks to test whether agents can observe state, operate systems, and comply with hard safety constraints throughout—not merely answer aviation knowledge questions. GPT-5.6 Sol achieved the highest success rate, but similar knowledge scores did not correspond to similar task success rates, showing that static question answering cannot replace interactive safety testing.

2026-08-19

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Reconstruction Hides Papers and Provides Only Older References; Multi-Model Review Raises Research-Idea Match Rate from 14.6% to 35.6%

A new benchmark asks models to reconstruct a hidden paper’s core research idea using only references that existed before its publication. Cross-review by four models and Swiss-system selection improve the match rate, but the candidate count, judge pool, and compute budget are not aligned with the single-model setting.

2026-08-19

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BaT Schedules Reinforcement Learning Around Agent Failure Stages, Raising a 9B Policy’s AutoMedBench-Lite Score From 19.9 to 53.4

Benchmark-as-Teacher turns stage-level evaluation scores directly into the curriculum for the next training round, while using isolated synthetic data to prevent test answers from leaking into updates. The paper reports that its 9B policy substantially outperforms standard GRPO, but the full training dataset has not yet been released, and cross-system rankings mix different execution frameworks.

2026-08-19

FabriMAE 以視覺注意力熵判斷機器人動作可靠度，十分支選擇令成功率升至 86.8%

FabriMAE 從 VLA 模型內部的視覺注意力熵產生可靠度分數，不需另訓練驗證器或重複執行機器人軌跡。PI0.5 在 LIBERO-Plus 的成功率增加 1.1 個百分點，但目前證據仍限於模擬環境，論文連結的程式庫亦尚未公開存取。

2026-08-18

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PDDLCoder Lets Agents Iteratively Fix PDDL, Achieving an 89.6% Executable Success Rate on 106 Unseen Planning Problems

Rather than requiring a language model to directly generate long-horizon action sequences, PDDLCoder first constructs a PDDL world model that can be verified by a symbolic planner. DeepSeek V4 Flash solved 95 of 106 problems across four held-out domains, but results for different base models ranged from 89.6% to zero, showing that the framework’s effectiveness depends heavily on a model’s ability to follow formatting requirements and use tool feedback.

2026-08-18

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FluxBin Uses Binary Bases with a LUT CUDA Kernel, Reports Up to 5.92× Faster Inference on a Single A100

Rather than expanding ultra-low-bit weights back into FP16, FluxBin uses binary patterns to directly index precomputed partial sums. The paper reports up to a 5.92× speedup and a 10.19× improvement in energy efficiency on a single A100, but its public code link remains inaccessible.

2026-08-18

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Kozuchi Takes the Same 27B Code-Repair Agent to Java, Solving 41 of 128 Multi-SWE-bench Tasks

Fujitsu ported its fine-tuning-free Qwen3.5-27B setup, eight-stage repair workflow, and cross-agent test selector to Java unchanged. It solved 374 of 500 tasks on the Python SWE-bench Verified benchmark, but achieved only a 32.03% success rate on Java, showing that agent-framework portability does not imply equal language proficiency.

2026-08-18

PertMind Trains a 4B Model on Cellular Perturbation Endpoints, Turning 100 Million Single-Cell Profiles into Reinforcement Learning Rewards

Starting from Qwen3-4B, PertMind combines gene responses, pathway direction, and output formatting into computable rewards, eliminating the need to manually author large numbers of biological reasoning traces. The model transfers zero-shot to inverse perturbation identification and dual-perturbation tasks, but its natural-language mechanistic explanations still cannot be treated as causal proof.

2026-08-18

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R³-Bench Gives Six Problems a Shared Reasoning Budget; Models Fall Below Their Own Offline Upper Bound in 71 of 72 Test Settings

The new evaluation no longer provides a separate allowance for each problem. Instead, models must allocate tokens or tool calls across six problems. Even though all six models can solve the problems individually, under a shared budget they generally fail to direct compute toward the problems with the highest expected payoff.

2026-08-18

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RelArena-α Uniformly Reruns 21 Relational Data Tasks, With TabPFN-Rel Leading Among Models Using Standardized Tuning

Prior Labs has open-sourced a unified workflow for data splitting, hyperparameter tuning, and evaluation, enabling a fresh comparison of graph models, relational Transformers, and tabular methods. TabPFN-Rel shows that aggregating relational data before passing it to a tabular foundation model remains competitive, although the CPU cost of feature synthesis is not fully included in the leaderboard’s timing results.

2026-08-18

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FreeToken Dynamically Splits MoE Cache Misses, Runs a 35B Model at 39.3 token/s on an 8GB RTX 4060

Based on host memory and PCIe bandwidth, FreeToken dynamically decides whether experts missing from the cache should be transferred to the GPU or computed directly on the CPU. The paper reports 1.3× to 2.1× higher decoding throughput than the strongest baseline across five consumer-grade systems, but the linked code repository is not currently publicly accessible.

2026-08-18

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Success Rates Hold After Agent Context Compression, but GPT-5.5 State Rechecks Still Triple

A new evaluation separates tool calls into state retrieval and task execution, finding that context compression can sharply increase an agent’s rechecking costs before success rates decline. Under fivefold compression, GPT-5.5’s completion rate changed from 80% to 85%, while its average retrieval calls rose from 21.0 to 63.9.

2026-08-18