跳到正文
研踪PaperTrail

论文库

检索计算机领域论文，按研究方向、作者或关键词筛选。

部分采集异常
正在读取账号与研究记录…

全站更新概况

+0新增论文
+0其中 AI 相关
+0已有论文修订
+100新增 AI 导读

arXiv 最近完成扫描：2026-11-15 17:01 · 全站最近入库：2026-11-14 15:09各信源进度 →

统计口径与数据新鲜度

统计 2026-11-14 17:51 — 2026-11-15 17:51（北京时间） · 点击数字查看对应简报；AI 相关包含在新增论文内。

最近完成检查：2026-11-15 17:01最近发现新增版本：2026-11-14 15:09库内论文最新更新时间：2026-11-13 02:59

每分钟刷新已保存的数据。空检查不代表采集异常；新收录可能包含历史论文。AI 导读按新增版本计数。

23,348 已收录论文19,580 AI 相关6,729 有修订112 HF 社区发现

浏览论文库

研究日报 / 周报 →按信源浏览 →

时间范围跟随排序：版本更新、本站收录或首次发表。最新收录可能包含历史论文。部分信源异常，查看状态 →

找到 5 篇论文 · 按版本更新时间排序上轮扫描起点 2026-11-15 16:59（北京时间）
新论文 · v1arXiv:2611.15112

Elastra: A Simulation Framework for Elastic Bodies

Youran Sun, Chugang Yi, Xingyu Ren, Haizhao Yang

原文摘要

When robots interact with elastic surfaces such as mattresses and trampolines, changes in loading cause the surfaces to deform and rebound, altering subsequent contact locations and forces. We present Elastra, a simulation framework for modeling this interaction and supporting the training and evaluation of robot control policies. Elastra represents each surface using elements that move only vertically, with elastic connections and damping to model deformation and recovery. Contact forces drive surface motion, and the updated surface geometry is used to compute subsequent contact forces. We systematically validate our simulations against an independent implementation and a real mattress load curve. We further use Elastra to train the Unitree G1 humanoid to stand up on mattresses and trampolines. Using the pretrained HoST policy and ProtoMotions motion tracker as original controllers, we train residual policies through reinforcement learning to add corrections to their joint targets. Adding these residual policies increases the success rate with HoST from 44.1\% to 72.5\%, and from 55\% to 89\% on the trampoline. With ProtoMotions, the success rate increases from 19.0\% to 45.0\% for initial poses not encountered during training. The code and video are available on https://github.com/yichg/elastra.

cs.RO

查看论文
arXiv ↗PDF ↗
版本更新 · v2arXiv:2606.24177

Agon: An Autonomous Large-Scale Omnidisciplinary Research System Built on Prompt Economy

Youran Sun, Xingyu Ren, Chugang Yi, Jiaxuan Guo, Kejia Zhang, Jianda Du, Haizhao Yang

原文摘要

Large language models are making research scalable. We present \textsc{Agon}, a research orchestrator designed to probe the limits of automated research. \textsc{Agon} is built on six design principles: Prompt Economy, Future-Facing, Minimal Prompts, OmniDisciplinary, Massive Parallelism, and Zero-Code. We deployed \textsc{Agon} across domains using only brief topic descriptions as starting points and no human-written code. These deployments demonstrate scalability but also reveal previously unseen classes of failure that are difficult to address through prompt engineering. We organize these failures into a taxonomy along four dimensions: severity, fixability, visibility, and capability locus. Together, these results show that \textsc{Agon} is pushing research towards a new paradigm: machines scale, humans steer. Our code is publicly available at \href{https://github.com/AutoResearch-Factory/Agon/tree/lite}{this GitHub repository}.

cs.SE · cs.AI · cs.CL · cs.MA

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.11947

The Relative Value of Demonstration Data: Execution Time Limits and Operator Mixtures in Behavior Cloning

Jiaxuan Guo, Xingyu Ren, Youran Sun, Jingxin Yang, Kejia Zhang, Haizhao Yang

原文摘要

Robot demonstration datasets combine demonstrations from teleoperators of uneven experience, so collection teams need to know how many demonstrations from one group match those from another. We measure this with a data multiple: the ratio of the numbers of demonstrations two groups need for their policies to reach the same success rate. On robomimic's simulated Square and Can tasks, we train behavior-cloning policies on demonstrations from the two teleoperators robomimic labels Better and the two it labels Worse, varying dataset size and the share of Worse demonstrations. On Square, within robomimic's 25-second time limit, an all-Worse training set needs 1.55 times as many demonstrations as an all-Better set. Most of this difference is late completion: policies trained on Worse demonstrations finish later, as the Worse teleoperators do, and with a 100-second limit the multiple falls to 1.11. On Can, most of the difference remains at 100 seconds. If each Worse demonstration were worth 1/1.55 of a Better one, as in an all-Worse set, a half-Better, half-Worse set on Square would need 1.22 times as many demonstrations as an all-Better set. In fact, it needs 13% more than that, and this excess persists at every time limit from 20 to 200 seconds. The excess remains when training draws demonstrations uniformly instead of in proportion to their length. On Square, within the 25-second limit, switching to these Worse teleoperators is cheaper at equal success only if they earn less than a third of the Better teleoperators' hourly pay, because Worse demonstrations also take longer to record.

cs.RO · cs.LG

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.11899

How Far Must a Test Explore? Certifying Composable Read-Write Interfaces from Their Own Random Edits

Jiaxuan Guo, Xingyu Ren, Jingxin Yang, Youran Sun, Haizhao Yang

原文摘要

Random tests of stateful code check laws, like reading back a written value, at states that random writes reach. The laws hold at every state exactly when the code behaves like a record with hidden data, so a pass is taken as evidence. This can fail: the laws can hold at every sample while the code is far from any record. The laws are local, but behaving like a record is global. Self-testing and stability theorems bridge this gap when the query distribution is controlled by the tester or preserved by the tested maps, but here it is generated by the tested writes, which need not be invertible. We ask how far a test must explore. An interface has $m$ binary variables, each with a read and two writes; in a record, reads return their bits and writes set only their bits. Samples are the ends of geometric runs of mean $\ell$ of uniformly random writes from a start distribution; a tester adds its own write chains of depth $d$. The distance is the least, over records with hidden data, of the largest probability that a read or write differs from it at a sample. From one start state, law checks at $O(m^2\log m/ε)$ samples certify, at fixed confidence, any threshold $ε$ after geometric runs of every positive mean, while runs of fixed positive length can pass at distance $1/2$. For the worst start distribution, take $ε=m^{-β}$ with fixed $0<β\le1$. For $d\le cm$ with fixed $c<1/2$, a polynomial budget of samples and writes exists exactly when $\ell=Ω(m)$, where law checks suffice. With no runs, the least depth for this budget is $(1/2+o(1))m$. Counting samples alone, in the worst case at $ε=1/(32m)$ and $d\le m/16$, the critical mean for a polynomial budget is $Θ(m\log(m/d)/\log m)$, by exhaustive search. So one start state needs only law checks, and the worst case at these thresholds needs exploration linear in $m$ if writes are counted.

cs.DS · cs.LG · cs.PL · cs.SE

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.10050

Selected-Risk Calibration for Feasibility at Returned Decisions

Raghav Thind, Youran Sun, Ling Liang, Haizhao Yang

原文摘要

When an optimization problem uses a surrogate constraint, a decision that is feasible for the surrogate need not be feasible for the true constraint. We propose Selected-Risk Calibration (SRC), a framework that calibrates a learned constraint against the optimizer's returned decisions for objectives drawn from a given distribution. SRC guarantees that, with probability at least $1-δ$, every margin it certifies on held-out objectives has selected risk, the probability that the returned decision is infeasible, of at most $γ$. We also show that the random-input coverage guarantee of split conformal prediction does not bound the selected risk, and that likelihood-ratio reweighting for feedback covariate shift does not apply when the learned constraint is active at the returned decision. Our experiments show that a conformal margin covering about $90\%$ of random inputs covered as few as $17\%$ of the returned decisions, and every certified margin had a violation rate below its target on unseen objectives. Moreover, at equal cost, SRC's correction had fewer or equal infeasible solutions to the baselines in 57 of 62 comparisons. The SRC framework applies to any setting in which a surrogate model chooses a decision whose feasibility can be verified for a sample of instances.

cs.LG · stat.ML

查看论文
arXiv ↗PDF ↗