The paper "Intelligence Under Time Constraints: Rethinking Test-Time Compute" examines how to decide not only how much computation to perform but also when to start it in streaming interactions where evidence arrives incrementally and may change. It introduces the information‑slack dilemma, describing the trade‑off between early computation that has more time to finish but relies on incomplete evidence, and waiting for more information that reduces computational slack. The authors propose a research agenda focused on selective recovery under controlled evidence revisions, and suggest evaluation metrics that separate early‑execution effects, deployment value versus full‑input alternatives, and the added value of predictive policies while accounting for shared‑resource costs, aiming for trustworthy, on‑time responses within a declared resource envelope.
By Xiaotian Zhang (Trooly.AI)
arXiv:2606. 04402v1 Announce Type: new Abstract: Modern reasoning models can allocate different amounts of test-time computation, such as thinking tokens, model calls, or compute budget, to different tasks.
By Jingbo Wen, Liang He, Ziqi He
arXiv:2609.16302v1 Announce Type: cross
Abstract: When a coding agent returns to existing software, it inherits evidence from earlier engineering work: tests, type checks, proofs, static analyses, an...
By Anjan Goswami
arXiv:2609.39334v1 Announce Type: cross
Abstract: Test-time scaling has recently emerged as a powerful approach for improving LLM reasoning by allocating additional computation during inference, subs...
By Jinwoo Jeong (Korea University), Woohyung Choi (Korea University), Myeongjae Jeon (POSTECH), Jeongseob Ahn (Korea University)
The paper introduces RAISE, a diagnostic framework that tests whether a costly large language model (LLM) signal provides enough pre-call information to justify selective use. It identifies the failure mode of acquisition collapse, where an LLM appears useful overall but lacks actionable evidence for individual decisions. The authors demonstrate RAISE with Structured Hypothesis Embeddings (SHE) and evaluate it across multiple study designs, showing that predictable incremental benefit, rather than average lift, indicates recoverable selective value.
By Ying Yuan, Yu Wang, Yize Cheng, Xuyang Wu
arXiv:2608. 04001v1 Announce Type: cross Abstract: Large language models can solve substantially harder reasoning problems with more inference-time compute.
By Mohsen Hariri, Weicong Chen, Nahal Shahini, Vikash Singh, Kai Ye, Amirhossein Samandar, Debargha Ganguly, Sreehari Sankar, Yanyan Zhang, Shouren Wang, Jerry Peng, Biyao Zhang, Michael Hinczewski, Vipin Chaudhary
The paper introduces EasyProver, a Lean theorem prover that uses recursive agents, isolated contexts, asynchronous search, parallel tactic probing, and an elastic elaboration service. It achieves near‑saturation results on several benchmark suites, proving 669/672 PutnamBench problems, all six IMO 2026 formalizations, 597/600 NTP4VC verification conditions, and all 189 VERINA tasks. Additionally, the authors present Aegis, a pipeline that transforms competitive programming problems into harder verification tasks by generating safe Rust solutions, translating them to Lean, and producing 2,045 Rust‑Lean program pairs with 11,533 candidate specifications, enabling new benchmarks for provers and reinforcement learning.
By Debargha Ganguly, Divya Raghunathan, Dejan Jovanovi\'c, Vikash Singh, Soonho Kong, Aaron Tomb, Aman Goel, Ferhat Erata, Sam Bayless
arXiv:2607. 13034v1 Announce Type: new Abstract: Large language model (LLM) agents increasingly automate multi-step engineering and informatics workflows, yet they rarely ask how much effort a task actually requires.
By Junjie Yin, Xinyu Feng
arXiv:2609.23058v1 Announce Type: new
Abstract: Current agent runtimes that plan before acting generally execute a step once it becomes ready. We present LazyAgent, a unified execution framework for...
By Xin Heng
arXiv:2610.14146v1 Announce Type: new
Abstract: Long-horizon terminal benchmarks measure whether an agent can sustain progress through extended workflows with process rewards, not only whether its fi...
By Zongxia Li, Zhongzhi Li, Yucheng Shi, Ruhan Wang, Junyao Yang, Chengsong Huang, Zhichao Liu, Xiyang Wu, Anhao Li, Yue Yu, Ergan Shang, Fuxiao Liu, Tong Zheng, Ninghao Liu, Lichao Sun, Haitao Mi, Leowei Liang
The paper discusses how AI coding benchmarks suffer from both under‑specification, which forces solutions to guess or cheat, and over‑specification, which allows solutions to bypass intended challenges. It examines several repository‑level benchmarks that add hints from ground truth to mitigate under‑specification, but finds that this can inadvertently create spoilers. The authors experiment with different levels of specification and propose methods for generating concise, effective hints, which they plan to release to improve benchmark reliability.
By Yuanzhe Liu, Bastian Lipka, Avraham Shinnar, Jatin Ganhotra, Yao Ma, Martin Hirzel
ASPIRE introduces a non‑synchronized batched self‑speculative decoding framework for long‑context LLM inference, addressing the memory bottleneck of attention by drafting tokens with sparse attention and verifying them with full attention. It combines a unified mixed forward pass, a lightweight online speculation scheduler that lets each request independently decide when to verify, and an intra‑draft refresh layer that updates the sparse context at every draft step. Experiments on three models and five benchmarks show 1.70–4.58× speedup over autoregressive baselines and a 27% average improvement over the strongest prior self‑speculative methods.
By Amir Ziashahabi, Hossein Entezari Zarch, Lei Gao, Murali Annavaram, Salman Avestimehr