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Anthropic research found that when Claude Code was secretly biased toward one of two options on analytical judgment calls, 50 analysts matched its lean 86% of the time, frequently abandoning prior preferences and echoing the agent's arguments afterward.
Operon-S1 introduces a System 1-style fast decision controller that separates judging from doing in long-horizon biomedical LLM agents, posing 13 recurring judgements as closed questions answered by a local model in 0.6s median latency while cutting decision-related cost from 8.0%–35.2% with no measurable accuracy loss on five of six benchmarks.
This arXiv paper studies how LLMs anticipate the affective impact of gambling and stock-trading outcomes, finding diminished sensitivity to magnitude, an inverted loss aversion dubbed 'silicon gain amplification', and mappable-but-malleable mappings from outcomes to feelings in choice.
ThinkJev is a training-free method that builds fast (System 1) and slow (System 2) answering paths from a single hybrid-thinking open model, reading calibrated option probabilities directly or after variable-length thought, so it only pays for thinking where it statistically guarantees a target error rate.
A showcase of Mica v0.5 4B, a small local model that decomposes natural-language requests in Minecraft into chains of probabilistic multiple-choice decisions, enabling a bot to complete complex multi-goal tasks like gathering porkchops and crafting a netherite ingot from bare hands.
This paper proposes "Data-Driven Action Recourse" (DDAR), the first approach for generating actionable recourse in ML decision systems without costly human supervision, by building a dynamics model of historical user trajectories to find low-cost, plausible actions that achieve desired outcomes.
MedJev 是一个可在笔记本本地运行的开源 System-One 医疗模型，在十项医学基准上进行零样本评测，性能接近 GPT-4，并提出两参数再校准方法显著改善患者级概率校准。
这篇注册研究考察了自改进智能体将运行结果写成教训时，是否会在后续决策中传递运气（随机性）的影响。实验发现，在可靠工具因偶然失败后，LLM撰写的教训会让智能体在后续运行中显著更多地选择较差的工具（11.6% vs 0.4%），而修改记忆写入指令可降低这一危害。
The paper introduces PrimaCons, a four-axis benchmark for evaluating cross-primitive consistency in Jev, and proposes a Cross-Primitive Residual Calibrator (CPRC) that reduces inconsistency by 58.57% and improves accuracy by 2.97% while predicting decision failures with 0.91 ROC-AUC.
This paper introduces Iris, a multimodal System One architecture that extends typed decisions over text candidates to image candidates via independent per-candidate encoding, preserving order invariance and exact cache reuse while achieving strong accuracy on a visual-candidate benchmark built from Multimodal-Mind2Web, plus a 37×–191× latency reduction from candidate reuse and a fix for a Qwen3.5 vision tower Conv3d bottleneck under bf16 cuDNN.
该论文提出一个统一评估框架，使用 Jev 1.13.0 作为案例研究 Structured System 1 模型在时间序列分类、事件类型预测、油温方向及股价走势预测等任务上的表现，结果显示其在显式时间模式和事件预测上较强，但在许多数值序列分类和股票走势预测上接近随机水平。
A meta-analysis examining whether large language models exhibit human-like cognitive biases from prospect theory, specifically loss aversion, diminishing sensitivity, and probability weighting.
论文提出了一种名为 DIVA（Decision-Informed Value-of-information Acquisition）的训练无关方法，使 LLM 基于证据的决策价值来决定何时检索信息以及何时停止，从而避免为不改变决策的证据付费，并在四个领域、20 个 LLM 上将最佳基线的遗憾值降低 75%。
论文提出 SearchHorizonBench，一个用于评估 AI 智能体从执行经验中估计搜索价值的基准数据集，包含 45,632 次执行记录，发现历史校准可将 Brier 误差降低 16.2%，但历史估计器仅能解释不到 2% 的搜索增益方差。
研究表明乌鸦在随机点运动任务中评估视觉信息的速度是哺乳动物的两倍，揭示了没有新皮层的鸟类大脑如何编码决策。
This arXiv paper explores realistic models of change through a causal perspective, connecting decision-making frameworks with dynamical systems to better capture how systems evolve over time.
Perplexity releases pplx-decider-v1.2 and pplx-decider-flash-v1.2 under Apache 2.0, claiming 10x lower cost than GPT 6 Luna (low reasoning) with identical decision quality on the Artificial Analysis Decision Index. The new version is also 2.5x cheaper than Jev.
A benchmark put 17 AIs through 7,700 hands of heads-up no-limit Texas hold'em, finding that cheap reasoning LLMs matched frontier models, while a simple 30-line Python equity script beat dedicated decision models and held Claude Opus 5.5 to a draw.
System1-Arena 是一个包含 92k 问题、105 项任务与 138 个闭环环境的基准，用于比较 System-1 概率模型与 System-2 推理 LLM 的离散决策能力。结果显示最大的 System-1 模型在非智能体任务上以四分之一延迟匹配快速闭源 LLM 且校准更好，而推理 LLM 精度最高但成本约为 70 倍；在闭环环境中大型开源 LLM 成功率最高（达 32.9%）。
该论文研究语言模型能否预判自身决策的关键分歧点（cruxes），发现模型先验评估的 crux 与实际多 rollout 对比识别出的关键因素关联甚微，证明多 rollout 采样流程对于理解高风险决策不可或缺。