Symbolic Computation
Maximal rank of $4\times 4\times 4$ and $k\times 4\times 3$ tensors over $\mathbb{F}_2$
Jason Yang
We determine the maximum rank of $4\times 4\times 4$ and $k\times 4\times 3$ multidimensional arrays over the finite field $\mathbb{F}_2$. These results are made possible by a new method of enumerating non-isomorphic tensors.
Cryptography and Security · Cross-listed from quant-ph
Ordered-Angle Coding for Exact Multiuser Unanimity Testing
Luis Adrián Lizama-Pérez
We introduce ordered-angle coding for binary-unanimity testing among $n$ transformation-only users in a serial
quantum architecture inherited from Loop-Back communication. User $B_i$ fixes one private sign across a logical word and applies $R(s_i\alpha_j)$ in trial $j$. If $w$ users choose the negative sign, serial composition gives $R[(n-2w)\alpha_j]$. We show that every fixed same-axis pulse that is deterministic for both unanimous inputs under the binary Bell readout has $\alpha_j=q_j\pi/(2n)$ with integer $q_j$. For a nonadaptive word $\mathbf q=(q_1,\ldots,q_m)$, a mixed Hamming weight imitates the unanimous signature with probability \[ M_{\mathbf q}(w)=\prod_{j=1}^{m}\cos^2\!\left(\frac{\pi q_jw}{n}\right). \] Within this complete deterministic-unanimity pulse family, a finite perfect word exists if and only if $n$ is a power of two. For $n=2^r$, the dyadic word $(1,2,4,\ldots,2^{r-1})$ is exact and pulse-minimal with $m_{\min}=r=\log_2n$ trials. It replaces the $O(n^2\log(1/\varepsilon))$ worst-case burden of repeated smallest-angle tests by exact $O(\log n)$ verification. Odd
primes instead admit balanced statistical words with uniform mixed-weight imitation $2^{-(p-1)}$. For actual operations $R(s_i\alpha_j+\delta_{ij})$, an ideal rejecting position is lifted to $\sin^2\Delta_j$, where $\Delta_j=\sum_i\delta_{ij}$. This yields explicit robustness bounds with angular and binary-readout errors. Bell
entanglement is not required for the additive
algebra, but it keeps the traveler locally maximally mixed in every honest trial. We therefore present the result as a coded multiuser relation primitive with optional raw conference-key-candidate use, not as a composably secure conference-key protocol.
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Artificial Intelligence
The convergent laboratory: when AI reasoning, autonomous experiments, high performance and quantum computing reshape chemistry
Eliu Huerta, Xiaoyun Wang, Geetika Gupta et al. (+7 more)
This Comment emerges from TPC26 (https://tpc26.org), a conference convening leaders from academia, national laboratories, and industry who are reshaping materials science discovery. The meeting explored how AI, autonomous
agents, self-driving labs, higher performance and
quantum computing converge to amplify their individual impact on materials science discovery. The perspectives here reflect the firsthand experiences of researchers at these frontiers and capture the essence of this global endeavor. As AI-driven
reasoning, autonomous agentic frameworks, self-driving laboratories, and fault-tolerant
quantum processors mature simultaneously, we offer this Comment as a reference at what we believe is a tipping point of transformative advances and productive disruption in the chemical sciences.
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All authors Eliu Huerta, Xiaoyun Wang, Geetika Gupta, Edward H. Sargent, Cameron J. Owen, Victor Fung, Abhishek Mitra, Austin Cheng, Emma Bouchard, Shams Mehdi
Information Theory
A Nuclear-Norm Lower Bound for Dithered Scalar Quantization of Matrix Products
Piyush Sao, Narasinga Miniskar, Pedro Valero-Lara et al. (+2 more)
We consider the problem of minimizing error in quantized
matrix multiplication $C=AB$. Scalar quantization of the factors introduces rounding errors whose scale depends on the maximum absolute entries – the ranges – of their rows and columns. These ranges determine the quantization grid steps. To reduce the error, we optimize over product-preserving transformations that alter the factor ranges and grid steps without changing $C$. Specifically, we seek the smallest leading expected squared error over invertible inner changes of basis and orthogonal outer rotations. Under independent, zero-mean subtractive dither noise on an unbounded
lattice, we prove the output-only bound $E_{\rm lead} \ge (c_A+c_B)/K \Vert AB\Vert_*^2$, where $K$ is the inner dimension, $c_A$ and $c_B$ are normalized noise variances, and $\Vert AB\Vert_*$ is the
nuclear norm. The bound is tight: an SVD-aligned Hadamard construction attains the infimum whenever a Hadamard
matrix of order $K$ exists, including every power of two, while an SVD-aligned DCT construction is within a factor of two for every $K$. Without outer rotations, Gram-
matrix balancing minimizes factorization energy, and finite-set flattening achieves the bound within $C\log(K(m+n))$. For power-of-two $K$, conditional expectations deterministically select the Hadamard signs in $O((m+n)K^2)$ exact-real operations. Synthetic experiments verify both constructions and illustrate the tradeoff between regularization and conditioning. These results characterize the full-gauge optimum and quantify the cost of preserving row and column indices.
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All authors Piyush Sao, Narasinga Miniskar, Pedro Valero-Lara, Keita Teranishi, Sudip Seal
Computer Vision
Facial Age Estimation for Age Fraud Detection in National ID Systems
Sharib Athar, Arka Koner, Chetan Naik et al. (+4 more)
Identity fraud during biometric enrollment and updates remains a major challenge for large-scale national identity systems. A common fraud vector is misrepresenting one's age to access age-restricted services or welfare schemes. In this work, we present SwinAge, a facial age estimation system designed for use within the Aadhaar biometric enrollment pipeline, to assist quality-check (QC) operators to flag potential age-related fraud. This is critical for a system like Aadhaar (the world's largest national identity programme), that holds about 1.5 billion unique identities, with 22.4 million new enrollments and 283 million updates in the last year. Building upon the SwinFace architecture with landmark-based similarity (warp affine)
alignment, we train on a large in-house
dataset of 1.45 million face images and evaluate on an independent, age-stratified test set of 283K images, both drawn from an ethnically diverse population of 716K unique subjects. We investigate three Aadhaar-specific operational thresholds (5, 18, and 60 years) and propose a deployment triage framework that flags suspected cases for manual review. Following NIST FATE, we report false acceptance/rejection rates (FAR/FRR) at each threshold rather than aggregate accuracy: at 1% FAR the model achieves an FRR of 3% (<5yrs), 0.4% (>18yrs) and 11.0% (>60yrs). SwinAge achieves a mean absolute error (MAE) of 2.94 years on the same test set, outperforming three zero-shot
vision language models on all
benchmarks, and improving the state-of-the-art on 5 out of 7 public
benchmark datasets. We further report per-gender errors and distill lessons for national identity programs.
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All authors Sharib Athar, Arka Koner, Chetan Naik, Barada P. Sabut, Tanusree Deb Barma, Anoop M. Namboodiri, Anil K. Jain
Computation and Language
Better Together: Complementary Query Rewriting Under a Strong RAG Baseline
Sara Shanian, Xiaoqin Yi, Pavlo Ruban et al. (+1 more)
A popular way to improve
Retrieval-Augmented Generation (RAG) is to rewrite the user's question into several variants and search with all of them. We test whether this actually helps once the underlying search is already strong. Under one fixed, competitive pipeline (BGE dense
retrieval, cross-encoder reranking, and MMR diversification), we compare four query-rewriting strategies (S1-S4) against two strong LLM baselines (HyDE, Query2Doc) on three
datasets (HotpotQA, AmbigNQ, and the 512K-document EnterpriseRAG-Bench) over three seeds with paired-bootstrap significance tests. Our headline result is that rewriting alone is at best competitive with a strong baseline, but combining methods yields outsized gains because different strategies fail on different questions. A post-hoc union of four methods (S1+S3+S4+HyDE) improves HIT@10 over the baseline by +12.5 points on enterprise data (51.70 vs 39.22), and a five-method union reaches 52.98 (+13.8). Budget-matched controls capture only ~40% of this gain, confirming that complementarity, not
retrieval budget, is the primary driver. On HotpotQA the union adds +1.6 to +1.8 points (p<0.001), saturating the all-method oracle; on AmbigNQ the same fusion hurts (-2.4 below the best solo, p<0.001), and we analyze when and why. Because rewriting is expensive, we evaluate in simulation a confidence-gated router that runs rewriting only when the baseline's own top-1 score is low. It captures about half of the enterprise full-merge gain (+4.3 HIT@10) while paying rewriting cost on <40% of queries, and automatically declines to rewrite on AmbigNQ. A downstream answer-quality evaluation confirms the router improves F1 by +1.92 (p<0.01) at roughly 40% of the expansion cost. In short: treat query rewriting as a complementary coverage source, applied through cost-aware routing, not as a standalone replacement for a strong baseline.
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All authors Sara Shanian, Xiaoqin Yi, Pavlo Ruban, Kurt MacDonald
Computer Vision
Unsupervised Transfer Clustering for Mitigating Cold Start in Active Prompt Learning
André Camargo Portella, Samuel Felipe dos Santos and Jurandy Almeida
Vision-
Language Models (VLMs) are able to achieve impressive zero-shot classification performance by aligning visual and textual representations, but each new task still demands handcrafted
prompts. Active
Prompt Learning (APL) combines Active Learning (AL) and
Prompt Learning (PL) into a single framework, allowing for the usage of the VLM prior knowledge for iteratively querying the most informative images to be labeled. However, the cold-start problem is still relevant for APL methods, where the performance of the initial query can be worse than random sampling. While recent state-of-the-art APL methods mitigate with balanced sampling and
multimodal features, they rely on rigid, distance-based clustering to group these features. This simplistic approach can struggle to capture the complex, high-dimensional semantic distributions inherent to VLMs, leading to suboptimal query representativeness. Unsupervised transfer can be applied to these features as a possible alternative, since it is capable of inferring the underlying human labeling of a task without any form of supervision. This way, samples can be grouped in semantically coherent clusters. This paper proposes Unsupervised Transfer Clustering with Selective Querying (UTC+SQ), a framework that enhances a recent APL approach by leveraging state-of-the-art unsupervised transfer model. These models generate high-fidelity pseudo-labels that establish semantically meaningful clusters, allowing for the selection of more relevant samples. Experimental evaluations demonstrate that shifting from distance-based to projection-based clustering improves the representativeness of the queried subset, achieving accuracy gains in 6 of the 8
datasets tested.
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Distributed Computing
Echo: Merging Host Device Buffers to Avoid Redundant Data Movement on Unified Memory SoCs
Yuheng Zhu, Yanbo Zhao, Jiajia Li et al. (+1 more)
GPU applications on unified-
memory (UMA) edge platforms often inherit a discrete-GPU
memory abstraction in which they allocate one buffer for the CPU, another for the GPU, and copy data between them before and after GPU execution. On UMA hardware these buffers reside in the same physical DRAM pool, so the copies consume bandwidth, time, and energy. Despite the growing adoption of UMA platforms, this pattern remains common because production software stacks, libraries, and samples were written for portability across discrete GPUs. However, removing these copies is not as simple as merging the two buffers, because the original program may rely on the two buffers being distinct, or on the copy itself ordering CPU and GPU accesses. Echo removes these copies only when it preserves the data values and access ordering on which the program depends. It does so along two complementary paths: source-level rewriting and binary deployment. Echo-SR is a compile-time LLVM transformation that proves safety and rewrites accepted pairs in place. Echo-DR is a profile-guided binary optimizer for existing applications that profiles and validates stable allocation/copy patterns and, at runtime, intercepts the matching calls to unify profile-matched pairs while preserving the ordering effects of removed copies. Across seven
benchmarks on three NVIDIA Jetson platforms, Echo's benefit grows with the fraction of baseline time spent on copies. Copy-dominated workloads speed up by up to 7.05x, closed-source end-to-end applications speed up by up to 1.40x. On the five source-available Orin
benchmarks, both automatic paths recover >=99% of the gain of a manually optimized reference.
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All authors Yuheng Zhu, Yanbo Zhao, Jiajia Li, Man-Ki Yoon
Computation and Language
TamilEOT: A Dataset and Model for Semantic End-of-Turn Detection in Tamil Telephone Speech
Santhoshkumar V
A voice
agent must decide, at each pause, whether the speaker has finished or is still thinking. Without a model of the
language, systems fall back to a fixed silence timeout: too short, and the
agent interrupts; too long, and every turn pays the full wait. We release TamilEOT, to our knowledge the first open
dataset and detector for semantic end-of-turn detection in a South Indian
language: 18,485 labelled turn boundaries from 116 real Tamil telephone conversations, two audio-only detectors built on Smart Turn v3's architecture, and a turn-level companion set of 4,774 whole turns with their pause structure. On a held-out split of 4,168 clips from 30 unseen calls, accuracy rises from 70.30% zero-shot to 83.71% (8.7 MB) and 86.13% (21 MB); ROC-AUC rises from 0.751 to 0.921. Both run in under 150 ms single-threaded on a laptop CPU. We also report what building it cost. Our rule-derived negative-class labels scored 44.4% against a blind human pass, below chance, because the rule answered a different question than the model is asked; an audio-LLM labeller at 97.5% agreement replaced them for US$5.69. Of six training levers, only encoder capacity moved the result: three identical runs spanned 0.87 accuracy points, the floor below which nothing else here is a finding. Replaying labelled boundaries through the production VAD and streaming adapter costs a further 2.60 points, and 7.8% of boundaries never reach the model at all. Data, weights, code and every negative result are public.
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Cryptography and Security
Reaching the Cards Apple Wallet Leaves Behind: Direct NFC Acquisition of PRO100, HUMO and UZCARD Payment Cards on iOS
Gusein Djalilov
Contactless payment from a phone has become routine in many
markets, but the convenience is unevenly distributed. Apple currently lists Kazakhstan among supported Apple Pay
markets, but not Uzbekistan, Kyrgyzstan, Tajikistan or Turkmenistan. In Uzbekistan, HUMO and UZCARD are the two national interbank retail card systems, and both include contactless card products. This paper describes an application-level card-capture method for tested PRO100, HUMO and UZCARD cards on NFC-capable iPhones. Using Core NFC, the reader opens a tag session, selects an application when necessary, and recovers the primary account number (PAN) and expiry date from the returned data. The difficult parts were empirical: identifying AIDs that worked on the tested cards and decoding card-generation-specific response layouts. The implementation was later hardened around a hybrid parsing path that prefers BER-TLV/EMV fields when present and keeps the observed offsets only as a legacy fallback. The paper also gives a system-level workflow, a threat model, explicit failure handling, a platform comparison, and a reproducible evaluation protocol. No claim is made that the same behavior is available unchanged on every iPhone/iOS combination, on iPadOS or macOS, or under future Core NFC policy. The method captures registration data; it does not emulate a card, authorize a payment, or treat an NFC read as proof of ownership.
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Information Theory · Cross-listed from stat.ME
Bias-Corrected Subspace Intersection: Minimax-Optimal Shared Subspace Estimation in Multi-View Data
Xianwen Song, Yuepeng Yang and Cong Ma
Estimating a low-dimensional subspace shared across noisy data
matrices is a fundamental problem in multi-view
matrix estimation. We study this problem under the two-view JIVE model, where each data
matrix contains shared and view-specific low-rank components. We demonstrate that standard plug-in subspace intersection, including AJIVE, suffers from a second-order bias caused by direction-dependent leakage of the empirical singular vectors. We propose bias-corrected subspace intersection (BCSI), which removes this bias before estimating the shared subspace. We establish finite-sample risk bounds for BCSI that accommodate unequal view dimensions, signal strengths, and view-specific ranks and require no condition-number assumptions on the signal
matrices. When the shared and view-specific ranks are comparable, these bounds match our minimax lower bounds up to universal constants. The resulting minimax rate contains a new second-order term, arising from quadratic leakage perturbations relative to the shrinking spectral gap when the view-specific subspaces are nearly aligned. This term is absent from previous JIVE minimax lower bounds. Numerical experiments demonstrate the advantage of BCSI over AJIVE when the leakage bias is pronounced. Along the way, we establish a nonasymptotic concentration result for the bias-corrected leakage Gram
matrix of a rectangular spiked
matrix, which may be of independent interest.
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Computer Vision
Time-Aware Assistive Navigation
Masaki Kuribayashi, Zhongkai Shangguan and Eshed Ohn-Bar
Can interactive
vision-and-
language agents learn not just what to say but also \textbf{when} to say it? Current
language models rarely plan over whether and when to realize a real-time response to a user. However, providing accurate and timely support for human decision-making, such as when guiding visually impaired individuals through urban environments, requires careful real-time responsiveness–poorly timed responses can distract users or add unnecessary cognitive load. As a machine intelligence challenge for
Multimodal Large
Language Model (MLLM)-based
agents, we introduce a large-scale
multimodal benchmark for an
egocentric, assistive
navigation task in complex outdoor environments. Using this
benchmark, we uncover a fundamental limitation of off-the-shelf MLLMs in delivering safe and time-sensitive
navigation instructions, even with model fine-tuning on substantial amounts of data. We then demonstrate that a simple yet effective modification of the model, including direct supervision to predict the underlying reason for each instruction, yields significant performance gains across open-loop, closed-loop, and sim-to-real generalization settings. However, our analysis highlights persistent challenges in temporal
reasoning, safety-critical object awareness, and relational and distance understanding. To advance the development of scalable assistive
agents, we will release our simulation,
benchmark, and code (available at the project website: https://timeli-icra.github.io/).
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Computer Vision
SceneMosaic: Efficient and Diverse Simulation-Ready Scene Generation via Hybrid Agentic Layout Evolution
Xingjian Ran, Xiaoye Mo, Sihao Liu et al. (+3 more)
Diverse and simulation-ready indoor scenes are essential for interactive entertainment and
embodied AI, yet their scalable generation remains challenging. Recent agentic text-to-3D scene pipelines that rely on
vision-
language models (VLMs) can generate scenes of high fidelity but require costly iterative object placement and refinement. Another mainstream paradigm, parametric image-to-3D scene models, produces scenes efficiently from strong priors learned from 2D images but often leads to imprecise and physically invalid scenes. More importantly, both paradigms struggle to output diverse scenes for a single input, making it hard for them to reflect the dynamically changing nature of real scenes. In this paper we propose SceneMosaic, a framework that combines the merits of both paradigms. It obtains the initial candidate from the learned image-based prior, and subsequently evolves the result through VLM
agents, ensuring both efficiency and physical validity. Within the
evolution process, SceneMosaic exploits the locality of natural scenes and decomposes a scene into independent local units, allowing separate
evolution within each unit before composing the global scene via Cartesian product. On SceneEval-100, SceneMosaic matches the strongest agentic baseline in semantic layout quality with a 24x speedup, substantially reduces physical violations, and receives the highest human ratings. Our code is publicly available at https://github.com/rxjfighting/SceneMosaic.
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All authors Xingjian Ran, Xiaoye Mo, Sihao Liu, Jianyu Zhang, Li Luo, Bo Dai
Robotics
Adaptive Cost-Sensitive Machine Learning for Autonomous Robot Navigation Failure Prediction: When Not All Errors Are Equal
Rifa Ferzana
Autonomous
robot navigation failures differ not only in categorical severity but also in the physical context in which they occur. A near-miss at low speed under reliable sensing is not equivalent to the same event during rapid motion, close obstacle approach or degraded perception. This paper reframes
navigation failure prediction as consequence-sensitive
forecasting. We first establish a fixed baseline in which training weights are modulated by categorical severity, then introduce an adaptive extension defining a state-dependent consequence function combining severity with normalised velocity, obstacle proximity and sensing uncertainty, together with a risk-sensitivity term that rises as conditions deteriorate. We evaluate on 2,000 simulated differential-drive episodes (~1,000,000 timesteps) using episode-level GroupKFold, with external validation on the UCI SCITOS G5
dataset. Fixed weighting raises Logistic Regression high-severity recall from 0.851 to 0.985 and reduces missed consequence cost from 1,940 to 313; the adaptive extension reaches 0.998 and 82. Under matched false-positive conditions, however, the discriminative advantage is modest (0.986 versus 0.984), so most of the gain reflects a more conservative operating point rather than better ranking. The effect is consistent across all five folds and stable across a threefold span of context coefficients. Because the primary simulation produced no collisions, we add a controlled extension in which 108 of 600 episodes terminate in contact: collision recall rises from 0.850 to 0.966 (fixed) and 0.984 (adaptive), with missed collision cost falling from 1,000 to 105, at false-positive rates of 0.413 and 0.799, respectively. Context-dependent consequence modelling thus provides a principled mechanism for allocating conservatism by physical risk.
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Sound
What Did I Just Say? Self-Listening for Full-Duplex Speech Models
Xuanning Zhou, Junyi Ao, Xiaotong Liu et al. (+3 more)
Full-duplex spoken
language models can listen and speak simultaneously, enabling them to handle interruptions and backchannels in human conversation. However, text generation,
speech synthesis, and audio playback proceed asynchronously. As a result, what a model believes it has said may not match what has actually been played to the user. We refer to the problem of recovering from an interruption while remaining aware of the model's realized
speech as anchor interruption. To address this problem, we propose Self-Listening, a full-duplex modeling approach that interleaves user
speech, model text, and the model's played
speech. By feeding the realized
speech output back to the model as an input stream, self-listening grounds interruption recovery in what the user has actually heard. We further introduce AnchorSpeech, a collection with homogeneous training and test splits for tracking which items of structured ordered responses have actually been spoken. AnchorSpeech-test evaluates whether a model can respond consistently with the last completed item before an interruption. Experiments show that, compared with full-duplex baselines, models equipped with self-listening mechanism achieve better anchoring performance.
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All authors Xuanning Zhou, Junyi Ao, Xiaotong Liu, Tom Ko, Benyou Wang, Haizhou Li
Artificial Intelligence · Cross-listed from physics.soc-ph
WolfSociety: Understanding Collective Risk from Harmful-Agent Scaling in Financial Agent Societies
Lejun Zhang, Sarah Lu-Liang, Xin Jiang et al. (+3 more)
Safety evaluations typically focus on individual
agents, but interacting
agents can spread harmful information and influence the environment in which later decisions are made. We study how collective failure changes with harmful-
agent fraction and society size in a controlled financial
agent society, where
agents communicate over a social network and trade in a shared
market. In the primary financial scenario, collective failure requires broad harmful
diffusion together with severe price dislocation or liquidity stress. Across all tested society sizes, failure remains rare at low harmful fractions but rises sharply over a narrow range. As society size grows from N=100 to N=2000, the harmful fraction associated with a 50% failure probability decreases from 4.7% to 2.2%, while the corresponding number of harmful
agents increases from approximately 5 to 44. In contrast, when the number of harmful
agents is held fixed, their impact becomes weaker as the society grows. Controlled interventions further show that broader network reach shifts the collapse boundary toward lower harmful fractions, whereas stronger conformity alone has little effect. To characterize these effects, we introduce
Agent Society Dynamics, a finite-size framework for relating harmful-
agent fraction, society size, and interaction structure to collective failure. Overall, our results reveal a nonlinear, size-dependent collapse transition in financial
agent societies, showing that collective failure depends not only on the prevalence of harmful
agents but also on the size and interaction structure of the surrounding society. Code is available at https://github.com/SAIL-Research-Lab/WolfSociety.
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All authors Lejun Zhang, Sarah Lu-Liang, Xin Jiang, Muning Wen, Weinan Zhang, Shangding Gu
Data Structures and Algorithms
Size-Sensitive Padded Decompositions for Faster Deterministic Negative-Weight Shortest Paths
Khoi Duong
We give a deterministic
algorithm for single-source shortest paths in directed graphs with integral edge weights at least $-W$ that runs in time $$O((m+n\log\log n)\log^2 n \log(nW)).$$ This improves the previous fastest deterministic bound of Li by a factor of $\log n$.
Robotics
GE-Act 2.0: Pretraining and Scaling a World-Action Model for Robotic Manipulation
Renhang Liu, Wenzhi Zhao, Zhuo Yang et al. (+42 more)
World-action models (WAM) predict future states to guide
robot actions, enabling learning from both action-free
video and action-labeled interaction. Most inherit pretrained
video generators, leaving WAM pretraining and scaling underexplored. We introduce Genie Envisioner Act 2.0 (GE-Act 2.0), a world-action model whose trainable generative and action components are all initialized from scratch on manipulation data. It combines a control-oriented autoencoder (CoAE), a single-step visual planner (SVP), and an inverse dynamics model (IDM). CoAE retains action- and instruction-relevant information under aggressive compression, while SVP produces a complete future state in one differentiable pass, so visual planning and inverse dynamics can be pretrained separately on complementary data. The components are then jointly trained with knowledge-aligned selective optimization (KASO), which reduces mismatched supervision by selecting only predicted futures judged behaviorally compatible with the recorded action. We evaluate pretrained checkpoints directly, without per-task fine-tuning, on 100 tasks across 20 manipulation skill groups with held-out scenes, backgrounds, lighting, and object instances. Scaling co-training data from 300 to 30,000 hours raises success from 17.1% to 44.1% on G1-OP and from 13.4% to 31.1% on G2-90D; despite comprising less than 2% of the co-training data, G2-90D improves by 17.7 points, suggesting cross-embodiment transfer. Gains span 19/20 and 18/20 skill groups, and skill-specific coverage strongly correlates with zero-shot out-of-distribution (OOD) success (Pearson r=0.80; Spearman rho=0.85). Under the same protocol, the model grounds object, color, shape, and position references in at least 90% of trials and follows explicit instructions even when they conflict with an already-committed behavior or a conventional scene association.
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All authors Renhang Liu, Wenzhi Zhao, Zhuo Yang, Liliang Chen, Pengfei Zhou, Shengcong Chen, Guanghui Ren, Youlun Peng, Rongjun Jin, Nan Wang, Sukai Wang, Xindong He, Jinyuan Feng, Ziyu Xiong, Linqing Zhong, Yifei Wei, Feng Han, Long Zhang, Da Huang, Nanshu Zhao, Chenghao Yin, Mo Wu, Zhaodong Yan, Kongtao Hu, Yuxiang Yan, Aogelijiang Niyazi, Yu Fang, Jia Zeng, Lizhu Meng, Daizhen Lv, Haoyu Cao, Zhiwen Hou, Lianjin Ye, Yuehan Niu, Zhikai Cai, Xuan Hu, Hui Min, Xiongfeng Cai, Yue Liao, Jing Wu, Soujanya Poria, Ye Li, Sanping Zhou, Maoqing Yao, AgiBot Research Team
Artificial Intelligence
Agents' Overreliance on Unreliable Tools
Hoyeol Yang, Woojung Song, Taewon Kim et al. (+3 more)
LLM
agents use tools to access information and perform computations beyond their parametric knowledge. Existing tool-use
benchmarks evaluate whether
agents select and call the right tools, assuming that tool returns are reliable. However, tools can return plausible but incorrect outputs. We evaluate 14 models with three tools (web search, an LLM sub-
agent, and a code executor), corrupting their returns to examine whether
agents overrely on unreliable tools.
Agents adopt corrupted returns at high rates, with mean adoption exceeding one third for every tool and reaching 68.0% for web search. Corrupted returns also frequently override correct answers that
agents give without tools, and this reliance persists in more complex tasks and in tasks that combine multiple tools.
Agents often make more tool calls under corrupted returns, and
reasoning traces show them questioning a return and even stating the correct answer. Nevertheless, they still pass the corrupted content to users, rarely warning them of the conflict. We further test interventions spanning
prompts, tool metadata, post-training, and activation steering. Verification
prompts reduce adoption across all three tools while largely preserving accuracy with correct returns, and reliability labels provide partial mitigation in web search. The tested post-training methods offer limited gains, and the effect of activation steering depends on the model and intervention strength. Overall, our findings provide a basis for diagnosing tool overreliance and for developing
agents that assess tool returns rather than assuming their reliability.
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All authors Hoyeol Yang, Woojung Song, Taewon Kim, Jonghyun Song, Seoyeon Park, Yohan Jo
Robotics
Benchmarking Dexterity of Multifingered Robot Hands: A Review and Perspective
Anthony Shilati, Anunth Ramaswami, Luke Batteas et al. (+16 more)
Robot hands are a key interface between AI and the physical world, making advances in robotic dexterity essential to realizing the
vision of physical AI. While impressive dexterity has been demonstrated with simple grippers, multifingered hands offer the potential for substantially greater versatility, precision, and adaptability in manipulation. In this review, we survey the state of the art in benchmarking the dexterity of multifingered
robot hands. Recognizing dexterity as a complex and multifaceted concept, we present the perspective of the U.S. National Science Foundation HAND Engineering Research Center, with a particular focus on fine in-hand manipulation. We introduce a framework consisting of three
benchmark levels that correspond to increasing system complexity, review representative
benchmarks at each level, and propose new
benchmarks and metrics to address limitations in the literature. More information can be found at https://hand-erc.github.io/benchmarking/.
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All authors Anthony Shilati, Anunth Ramaswami, Luke Batteas, Sylvia Tan, Anthony Barcio, Sairam Umakanth, Preksha Rao, David McDougall, Landry Graves, Ahmet A. Ozkan, Yunsoo Yoon, Arushi Pradhan, Michael G. Henry, Rohan Kota, Damian Gonzalez, Gray C. Thomas, Gary K. Fedder, J. Edward Colgate, Kevin M. Lynch
Computer Vision
Márcus Lobo, Vitor Matias, Afonso Paiva et al. (+3 more)
Vision-
Language Models (VLMs) are reshaping computer
vision by aligning visual and textual
embeddings, allowing models to recognize visual concepts and reason about them using natural
language. Traditional 3D deep-learning models, however, are typically trained for specific tasks, such as classification,
segmentation, or detection, and do not naturally support cross-modal
retrieval from their
embedding spaces using text or images as queries. To address this issue, Contrastive
Language-Image Pretraining (CLIP)-based methods align 3D
embeddings with pretrained image and text representations, giving rise to 3D
Vision-
Language Models (3D VLMs) that support zero-shot classification, cross-modal
retrieval, and open-vocabulary recognition of 3D shapes. This tutorial provides an overview of 3D VLMs, ranging from basic definitions of 3D representations and their encoding into
embeddings to cross-modal contrastive
alignment, modern
multimodal frameworks, and 3D
Vision-Large
Language Models (3D VLLMs). We present the main definitions of contrastive learning for
multimodal embedding alignment and highlight recent advances in
language-guided 3D Gaussian splatting, 3D shape generation, and
embodied AI for robotics.
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All authors Márcus Lobo, Vitor Matias, Afonso Paiva, Jeová Farias, Tiago Novello, Moacir Ponti
Machine Learning
HB-PVI: A Hierarchical Bayesian Personalization and Value-of-Information Framework for Complex Activity Recognition
Hammed A. Olayinka
Personalization can improve activity-recognition performance, but participant-specific gains are heterogeneous, and every additional calibration label has an acquisition cost. This study presents HB-PVI, a hierarchical
Bayesian personalization and value-of-information framework jointly modeling participant heterogeneity, the benefit and harm of four personalization mechanisms, and the economic value of an additional label, for the 47-participant MUSIC-CAR complex-activity cohort. A leakage-safe, leave-one-participant-out evaluation combines a sequential-Monte-Carlo participant-effect updater with a Student-$t$ hierarchical gain model and a one-step expected-value-of-sample-information (EVSI) stopping rule. Adapter personalization produced small positive mean F1 gains, growing from 0.00099 at one label to 0.00198 at ten, while adapter-plus-head and prototype-residual personalization were negative on average. Under the primary practical-benefit threshold ($\Delta_{\min}=0.01$) and cost setting, one-step EVSI was zero at every decision state, so the policy purchased no labels and retained population inference for all 47 participants, matching always-stop exactly (region-of-practical-equivalence probability $=1$). Relative to fixed ten-shot adapter personalization, this reduced labeling by 100% while keeping the posterior mean F1 loss at 0.00217 (95% credible interval, 0.00048 to 0.00389), with posterior probability 0.9992 of remaining below the 0.005 tolerance. HB-PVI was utility-optimal in 199 of 216 cost-threshold settings and in every setting at or above the primary label cost. These results argue for a population-first deployment policy whenever personalization gains are small relative to labeling, computation, and harm costs, and show that value-of-information
reasoning, not raw predictive accuracy, should drive personalization decisions in health-sensing applications.
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Distributed Computing
Sharpedo: Dual-Mode Uncertified DAG-Based Consensus Protocol
Zeno de Angeli
Mysticeti and Mahi-Mahi represent leading approaches to consensus protocols, leveraging a novel uncertified Directed Acyclic Graph data structure to achieve substantial performance benefits compared to prior work. Mysticeti and Mahi-Mahi differ in their underlying network assumptions, where the former makes assumptions about timing bounds for communication while the latter does not. Mysticeti trades robustness for lower levels of latency in ideal conditions, whereas Mahi-Mahi favours increased latency in order to ensure stronger progress guarantees in asynchronous settings. Although dual-mode protocols exist (i.e., protocols that can adapt do different network assumptions), none have been implemented to take full advantage of the novel data structure seen in the aforementioned protocols. Thus, our research project aims to trace the progression of consensus protocols, analyse how they address network assumptions constraints, and explore dual-mode protocols. Then, we present Sharpedo, the first dual-mode uncertified Directed Acyclic Graph consensus protocols which aims to achieve the best of both worlds: the high-performance of partially-synchronous protocols, while providing guarantees of progress even in asynchronous settings. Furthermore, Sharpedo integrates a novel dynamic scheduling mechanism resulting in improved system efficiency and performance optimization.
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Computer Vision
ViT3Flow: A Test-Time Training Transformer MeanFlow for Postoperative Radiograph Synthesis in Scoliosis
Rui Tang, Sicheng Yang, Moxin Zhao et al. (+6 more)
Predicting postoperative spinal morphology from preoperative radiographs could provide valuable support for scoliosis
surgical planning, but remains challenging because
surgical correction induces large spatial changes while anatomical structures must be faithfully retained. We formulate this problem as postoperative scoliosis radiograph synthesis and construct ScoliSurg, the first paired
dataset for this task, comprising 632 preoperative–postoperative whole-spine radiograph pairs with structured morphology information. We further propose ViT$^{3}$Flow, a single-NFE conditional MeanFlow framework for efficient postoperative radiograph synthesis. ViT$^{3}$Flow models
surgical correction as finite-interval generative transport and replaces conventional self-
attention with test-time-training
token mixers that perform sample-specific inner adaptation to the anatomy and deformity pattern of each case. In addition, a Spinal Morphology Extraction
Agent extracts distributions of dominant-curve region and direction from the preoperative radiograph. These distributions guide Diagnosis-Routed Interval Cross-
Attention (DRICA), which performs interval-dependent vertical, horizontal, joint, and global
retrieval from a separate preoperative
token stream. This design enables the evolving postoperative representation to incorporate spatially corresponding anatomical evidence throughout the transport process. Extensive experiments on ScoliSurg demonstrate that ViT$^{3}$Flow achieves the best performance among the compared methods in perceptual image quality, anatomical fidelity, and clinically relevant
geometric accuracy, while requiring only a single network evaluation. These results highlight the potential of ViT$^{3}$Flow for efficient and anatomically faithful postoperative radiograph synthesis in scoliosis
surgical planning.
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All authors Rui Tang, Sicheng Yang, Moxin Zhao, Hongqiu Wang, Guankun Wang, Lei Zhu, Hongliang Ren, Menglin Cong, Nan Meng
Artificial Intelligence
Planning and Scheduling Business Processes under Control-Flow Uncertainty: Extended Version
Michel Kunkler and Stefanie Rinderle-Ma
Scheduling activities in business processes can improve efficiency (e.g., reduce makespan), but is challenging because the exact sequence of activities required to complete a case is often uncertain due to decisions based on data that emerges during execution. Nevertheless, probabilistic information regarding such decisions can often be estimated or derived from historical execution logs, and can help anticipate which execution paths are likely to lead to successful completion. Planning with particular execution paths affects feasibility, i.e., the probability of successful completion, and the expected number of superfluous activities that are planned but never executed. We frame the problem as a chance-constrained optimization problem and present two formulations: A decomposed approach with two stages, a planning stage that minimizes the expected number of superfluous activities subject to a feasibility constraint, and a scheduling stage that minimizes the makespan over the planned activities; and an integrated approach that combines planning and scheduling into a single formulation. Evaluation on two real-world and one synthetic
dataset shows that the integrated approach yields superior makespans but is intractable at scale, while the decomposed approach scales to large settings.
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Artificial Intelligence
EnvCraft: Synthesizing Executable Environments in Agentic RL for Claw-like Agent
Yirong Zeng, Shen You, Jinhang Feng et al. (+8 more)
The paradigm of LLMs has rapidly shifted from passive
language interfaces to autonomous Claw-like
agents that execute long-horizon tasks across stateful workspaces. While Agentic Reinforcement Learning (Agentic RL) provides a promising path to optimize these
agents, its scaling is heavily bottlenecked by the severe scarcity of interactive training environments. Existing synthetic environments are strictly limited to tool-calling endpoints, rendering them insufficient for accommodating the end-to-end real-world demands of claw-like
agents. To bridge this gap, we introduce EnvCraft, an automated framework for synthesizing executable environments and scalable training data. Specifically, EnvCraft employs an environment synthesis engine to build sandbox-isolated workspaces, alongside a
topology-aware data generation engine to produce coherent task trajectories. Overall, we synthesize 139 interactive environments comprising approximately 20K complex tasks for Agentic RL training. Experiments on Qwen3/3.5 models (8B-32B) show that our method yields gains of up to +11.9% on Claw-style
benchmarks and +8.0% on general tool-use
benchmarks, with concurrent reductions in inference
token cost. The results confirm that synthesized executable environments provide robust and generalizable learning signals for training.
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All authors Yirong Zeng, Shen You, Jinhang Feng, Yufei Liu, Xiao Ding, Yutai Hou, Hao Cong, Yuxian Wang, Wu Ning, Wang Xu, Bibo Cai
Machine Learning
Capsule Lens: Locating and Tracking Concept Geometry in Model Representations
Yiming Tang, Harshvardhan Saini, Samyak Jha et al. (+3 more)
Understanding how concepts are encoded in the internal representations of machine learning models is a central problem in mechanistic interpretability, essential both for the science of deep learning and for the trustworthy deployment of increasingly capable models. Existing approaches to interpret model representations mainly map representations onto more interpretable spaces and do not directly characterize how concepts occupy representation space; various hypotheses have been proposed, but often lack of rigorous validation and largely focus on static representations. In this work, we introduce Capsule Lens, a framework that matches the region a concept occupies with a simple, trackable
geometric form, a capsule, defined by several interpretable parameters, fitted in closed form to each concept's geometry and validated on held-out samples. We apply Capsule Lens in two major settings: static and dynamic representations. On static representations, we demonstrate how to locate concept geometry across various models, and how the span and norm curves uncover important
geometric characteristics. On dynamic representations, we present three case studies tracking representation drifts induced by distinct training settings, CLIP pretraining, RL post-training on visual question answering, and RL post-training on mathematical
reasoning. These analyses reveal qualitatively different
geometric dynamics, ranging from broad network-wide restructuring in CLIP pretraining to localized and concept-specific changes in RL post-training. Our results include findings aligned with existing literature as well as novel observations. We believe Capsule Lens stands as a promising tool for locating, analyzing, and tracking concept geometry in both static and dynamic representations.
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All authors Yiming Tang, Harshvardhan Saini, Samyak Jha, Huaming Chen, Xufeng Duan, Dianbo Liu
Machine Learning
Multi-granularity Adaptive Hypergraph Representation Learning via Granular-ball
Sen Zhao, Yifan Guan, Jinyuan Ni et al. (+6 more)
Hypergraph representation learning aims to capture high-order information in graphs by constructing hyperedges that simultaneously connect multiple nodes. These hyperedges adapt to the graph's topological features, facilitating the extraction of high-order relationships at multiple granularities. Most prior work relies on predefined definitions to generate hyperedges, overlooking the diversity in graph topological structures and the multi-granularity characteristics of hyperedges. As a result, this limits their ability to effectively and adaptively discover high-order relationships and efficiently process complex structural information. To address this limitation, we propose a novel framework called \underline{M}ulti-\underline{G}ranularity \underline{H}ypergraph \underline{R}epresentation \underline{L}earning (MGHRL). MGHRL introduces an Adaptive Granular Hypergraph Generation strategy, which generates hyperedges at multiple levels of granularity through the adaptive splitting of granular-ball, effectively capturing high-order relationships based on the graph's topological structure. Additionally, we propose a Multi-Granularity Hypergraph Network with multiple sub-networks, capturing features from hyperedges at different granularities and integrating them via hierarchical reversible connections. Experimental results show that MGHRL significantly outperforms baseline models on
benchmark datasets.
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All authors Sen Zhao, Yifan Guan, Jinyuan Ni, Gaojie Xu, Zhang Xu, Xiaoyu Lian, Yi Liu, Yi Wang, Wei Wang
Artificial Intelligence
Deep belief networks are exact
Gleb Smirnov
We prove that every strictly positive probability distribution on \(\{-1,1\}^n\) is represented exactly by a sigmoid belief network with finite parameters. This answers a question of Sutskever and Hinton. The proof upgrades their probability-sharing approximation to exact representation using Brouwer's fixed-point
theorem.
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Software Engineering
Grounded Skill Synthesis from Code at Scale for Agentic Intelligence
Yongqi Tong, Pan Wang, Hang Wang et al. (+4 more)
Reusable skills give
agents transferable procedural knowledge, making scalable acquisition essential for extending
agents beyond prior experience. Existing methods face two limitations: trajectory-based synthesis requires interactions with specific environments, while document-derived skills may lack executable evidence and verification. Source code offers a complementary path: it requires no prior
agent experience yet provides executable evidence for grounding abstractions. We present Code2Skill, a fully automated pipeline that transforms selected code units into implementation-anchored records of atomic operations, composite workflows, and recurring patterns, then verifies each record through source-body-blind reconstruction and source-aware comparison. Applied to 19,769 popular, actively maintained GitHub repositories, Code2Skill produces CodeSkillBank, a grounded bank of 1,006,822 accepted records with workflow, boundary, provenance, and source-evidence metadata. Across 72 protocol-matched evaluations covering nine model settings and eight
benchmarks, models augmented with retrieved CodeSkillBank skills improve by 11.7% on average over matched baselines and outperform them in 57 cases. Under a unified downstream interface, Code2Skill also outperforms trajectory-derived skill banks on all seven shared
benchmarks, showing that repository-derived skills can provide useful procedural knowledge before
agents accumulate sufficient interaction experience. Skills synthesized from tested AI-generated code achieve a 93.50% pass rate, compared with 93.00% for human-written code, providing initial evidence that the pipeline can expand with the growing volume of AI-generated software. Overall, Code2Skill transforms procedural knowledge embedded in repositories into grounded, verifiable, and transferable
agent skills.
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All authors Yongqi Tong, Pan Wang, Hang Wang, Jianshe Li, Xin Zhang, Jiang-Ming Yang, Wei Wu