TYPEWRITERLM, a new model trained on 54 billion historical tokens before 1913, enhances understanding of the past while tackling data quality issues. This framework could transform historical research by connecting AI and the humanities. https://arxiv.org/abs/2606.02991
Pretraining Language Models on Historical Text arxiv.org
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Superintelligent AI, designed through a solipsistic lens, risks failing at cooperation due to undermining behaviors from interactions among adaptive agents. This challenges paradigms and calls for cooperative systems emphasizing human agency and institutional design. https://a…
Solipsistic Superintelligence is Unlikely to be Cooperative arxiv.org
Cognitive science is set for a breakthrough with AI integration, allowing generalizable models of cognition via naturalistic tasks. This method reshapes intelligence understanding, yielding insights and hypotheses about human cognition with complex data. https://arxiv.org/abs/…
[2502.20349] Naturalistic Computational Cognitive Science: Towards generalizable models and theories that capture the full range of natural behavior arxiv.org
Stanford's Spiral framework redefines language model training by merging sequential, parallel, and aggregative inference, boosting reasoning efficiency up to 15% over previous methods. https://arxiv.org/abs/2606.23595
SPIRAL: Learning to Search and Aggregate arxiv.org
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- SPIRAL co-trains three reasoning primitives in one RL framework: sequential chain-of-thought, parallel sampling of traces, and learned aggregation of those traces.
- The paper reports outperforming GRPO by up to 11× scaling efficiency and 15% higher performance when all three compute primitives are scaled.
- Training uses set reinforcement learning to make parallel traces collectively useful, plus standard RL to train the aggregation step itself.
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D-OPSD transforms training for step-distilled diffusion models, enabling on-policy self-distillation to learn new concepts without sacrificing efficient few-step inference. This enhances image quality and response speed for AI-generated content. https://arxiv.org/abs/2605.05204
D-OPSD: On-Policy Self-Distillation for Continuously Tuning Step-Distilled Diffusion Models arxiv.org
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- The paper argues ordinary supervised fine-tuning of step-distilled diffusion models compromises their inherent few-step inference capability.
- D-OPSD treats the model as both teacher, seeing text plus target-image information, and student, seeing only text features.
- The authors claim their approach lets models learn new concepts and styles without sacrificing original few-step capacity.
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A study questions users' well-formed preferences in AI interactions, introducing the COPREF model that emphasizes preference building through dialogue. The COSHOP benchmark shows agents fail to enhance user knowledge, limiting personalized recommendations. https://arxiv.org/ab…
Beyond expert users: agents should help users construct preferences, not just elicit them arxiv.org
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- New arxiv paper argues AI agents should help non-expert users construct preferences, not assume users already know what they want.
- The authors introduce CoShop, an interactive benchmark where no tested agent exceeded 56% accuracy after five turns of dialogue.
- Failures came from agents' limited knowledge expansion, not from difficulty finding items once preferences were specified.
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Findings show some language models, like Gemma-3-27B, exhibit 'latent planning' by forming representations that influence outputs. Detected via activation patching, this reveals model behavior complexity and enhances understanding of AI text generation. https://arxiv.org/abs/2…
Where's the Plan? Locating Latent Planning in Language Models with Lightweight Mechanistic Interventions arxiv.org
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- Across Qwen3, Gemma-3, and Llama-3 at more than ten scales, all families encode future rhyme info at line boundaries.
- Only Gemma-3-27B causally relies on that encoding; all other tested models show near-zero causal effect despite strong probe signals.
- Path patching localized Gemma-3-27B's planning handoff to five attention heads recovering roughly 90% of rhyme-routing capacity.
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This position paper argues against using AI for peer review, highlighting the risk of a "hivemind" effect that homogenizes feedback. It reveals "paper laundering" that inflates scores without true improvement, calling for strict evaluations before AI adoption. https://arxiv.or…
Stop Automating Peer Review Without Rigorous Evaluation arxiv.org
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- A new ICML 2026 oral position paper argues today's AI systems should not be used to produce paper reviews, grounded in ICLR 2026 data.
- AI reviewers cluster tightly: within-paper similarity runs 8.7 to 9.8 percent higher than human reviews, and across-paper 4.1 to 39.8 percent higher.
- Prompting an LLM to rewrite a paper lifted AI review scores by +0.45 on average (p
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Research shows higher weight decay in language model pretraining boosts downstream adaptability, improving performance despite lower validation loss. This finding challenges conventional optimization views, emphasizing model plasticity's importance. https://arxiv.org/abs/2602.…
Weight Decay Improves Language Model Plasticity arxiv.org
Innovative research uses detailed mouse brain connectomics to improve recurrent neural networks, showing that biological structure enhances learning performance and drives networks towards brain-like organization. https://arxiv.org/abs/2606.14975
Harnessing cortical geometry, wiring, and function as inductive biases for recurrent neural networks arxiv.org
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- Researchers built RNNs from spatial and connectivity data covering nearly 12,000 neurons from mouse visual cortex.
- Biologically constrained networks consistently outperformed baseline models across three cognitive decision-making tasks.
- Functional weight initialization, not spatial structure alone, provided the largest single performance boost.
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DriftXpress transforms one-step generative modeling by cutting training time while ensuring high-quality outputs. By applying projected RKHS fields, it sustains drifting models' effectiveness and enhances inference, promising a new era in efficient image generation. https://ar…
DriftXpress: Faster Drifting Models via Projected RKHS Fields arxiv.org
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- DriftXpress reports a 6.68x throughput speedup on SVHN training, from 2,307 to 15,402 images per second, with FID moving from 2.94 to 3.11.
- The method approximates the drifting kernel with a Nyström feature map from landmark points, while keeping generated-sample repulsion exact for stability.
- Speedups shrink on harder datasets: 6.63x on CIFAR10, 2.95x on CIFAR100, and 2.64x on ImageNet, where FID edges from 8.83 to 9.21.
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Researchers have advanced machine unlearning with near-optimal algorithms that reduce costs of data removal from models. Their findings promise significant accuracy gains over retraining, offering a new method to meet privacy needs without sacrificing performance. https://arxi…
Near-Optimal Pure Machine Unlearning for Smooth Strongly Convex Losses arxiv.org
