Computer Vision
Scaling Multi-Teacher Distillation for Digital Pathology
Sofiène Boutaj, Pierre Marza, Varun Belagali et al. (+3 more)
Multi-teacher
distillation allows transferring knowledge from multiple "teacher" networks to a single "student" network. This method is promising in fields such as digital pathology, where many powerful foundation models were proposed recently. However, as we show in this paper, the standard multi-teacher
distillation approach reacts poorly to an increase in the number of teachers used to train a student encoder. We demonstrate that learning teacher-specific representations is a key to scaling in MTD. Importantly, different design choices, such as learnable teacher
tokens, a tailored
attention scheme, additional mixture-of-experts layers and a contrastive loss, are proposed to better learn such teacher-specific representations. We show that our method better scales with respect to the number of considered teachers, allowing us to train compact student encoders, up to 10x more efficient than larger teacher foundation models, while matching their performance and even outperforming them on a large set of tile-level and slide-level tasks. We conduct a thorough empirical validation, evaluating more than 10 foundation models on 39 tasks spanning tile and slide levels. As a result, we release a new collection of strong and efficient foundation models, named OMNI, trained from the knowledge of 10 state-of-the-art foundation models. We finally conduct an analysis of the learned teacher-specific representations, highlighting their complementarity and explaining why they can be easily aggregated at downstream time.
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All authors Sofiène Boutaj, Pierre Marza, Varun Belagali, Dimitris Samaras, Maria Vakalopoulou, Stergios Christodoulidis
Machine Learning
DiffusionART: Efficient, Stable and Scalable Reinforcement of Diffusion Models
Zhenglin Wan, Jiayu Chen, Zhengfeiyang Zhang et al. (+6 more)
Online reinforcement learning (RL) fine-tunes
diffusion models with black-box rewards, but reverse-process methods such as Flow-GRPO are limited in efficiency by poor credit assignment, while forward-process methods such as DiffusionNFT are efficient yet unstable without carefully tuned exponential-moving-average (EMA) off-policy training. We show that NFT-style objectives contain a hidden forward–reverse consistency force that joint positive–negative training cancels only partially, and that EMA stabilizes training through restoration rather than off-policy rollouts. We introduce DiffusionART (
Diffusion Reinforcement with Aggregation-First Reward Tilting), which aggregates each
prompt's rewards into a shared reward-improving direction for reverse-process states, removing the consistency force by construction, and adds a trust-region-masked second update for drift-adaptive restoration without EMA tuning. It requires no differentiable reward model, remains effective with fewer samples per
prompt, and supports pairwise reward models (DiffusionART-Pairwise). Across image and
video generation, DiffusionART trains efficiently and stably without per-task tuning. In particular, on HunyuanVideo-13B with equally weighted visual and motion quality rewards, DiffusionART reaches the late-stage mean reward levels of DiffusionNFT and Flow-GRPO-Fast with 12.02 and 11.72 times fewer GPU hours, respectively, while achieving 28.8% and 53.0% higher mean training rewards.
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All authors Zhenglin Wan, Jiayu Chen, Zhengfeiyang Zhang, Pengfei Zhou, Wangbo Zhao, Fangyuan Kong, Pengfei Wan, Kun Gai, Yang You
Machine Learning
An all-transistor physical-learning transformer can be trained to perform in-context learning
Michael C. Abbott and Benjamin B. Machta
Most systems trained by
equilibrium propagation or coupled learning are in the same class as multi-layer perceptrons: while they may be universal function approximators, they struggle to learn associative tasks which are easy for
transformers.
Equilibrium propagation relies on a bidirectional flow of information through the physical system between input and output. The reverse flow is broken by more complex nonlinear systems, such as those performing multiplication which is needed to express dot-product
attention. Here we explicitly restore this bidirectional flow by introducing extra
circuit elements for this purpose into a double or dyadic
circuit. We then use these ideas to construct an
attention mechanism exploiting the exponential current law of sub-threshold MOSFET devices. We show that in simulations this
circuit can be trained to perform a simple in-context learning task, despite using realistic reverse components which transmit only an approximate gradient. For this training to succeed we need cross-
entropy loss (not mean squared error). We generate nudges appropriate for this loss using a novel floating
circuit element, based on the same MOSFET sub-
circuit used for softmax. We train the resistors in this
circuit using the Lion optimiser, which can be implemented using one capacitor per parameter to store local
memory of past gradient estimates.
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Computation and Language
Contextual Correction of Parallel Drafts for Speculative Decoding
Roy Eisenstadt, Harel Lidar, Tal Gonen et al. (+5 more)
Speculative decoding accelerates large
language model inference by using a lightweight drafter to propose multiple
tokens that are subsequently verified in parallel by the target model. Recent
diffusion-based drafters make this paradigm particularly attractive: deep
attention-based neural backbones provide strong predictions, while drafting multiple future positions in parallel avoids the sequential cost of autoregressive generation. This parallelism, however, comes at a cost.
Tokens within a draft block are predicted without access to the realized identities of preceding draft
tokens, removing intra-block dependencies that would naturally be available under autoregressive generation. We introduce DCOCO (DFlash Contextual Corrector), a lightweight mechanism that restores these dependencies while preserving parallel drafting. Rather than committing to each position's top prediction, DCOCO retains multiple candidate
tokens and evaluates their contextual compatibility across adjacent draft positions in parallel, enabling contextually informed
token selection at low additional cost. Across four verifier backbones and ten
benchmarks, DCOCO achieves the highest average throughput in every evaluated configuration, spanning greedy and
stochastic decoding with and without extended
reasoning, with speedups of up to 6.6x over vanilla decoding. Its advantage persists under concurrent serving: DCOCO outperforms the strongest competing method by 6-23% across 4-32 concurrent requests, while other sequential correctors fall below uncorrected DFlash at these loads. These results demonstrate that recovering intra-block dependencies through parallel contextual scoring delivers consistent acceleration across decoding regimes and serving loads.
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All authors Roy Eisenstadt, Harel Lidar, Tal Gonen, Omer Luxembourg, Itamar Zimerman, Lior Wolf, Eli David, Ilan Naiman
Computation and Language
SWE-Duel: Towards a Self-Scaling Arena for Coding Agents
Wei Minn, Zhensu Sun, Lwin Khin Shar et al. (+1 more)
Static coding
benchmarks are fixed at release. Over time their tasks leak into training data, and stronger
agents eventually saturate them. We argue that a
benchmark should instead aim to be self-scaling: its tasks and oracles are created at evaluation time, so no content exists in advance to leak, and its difficulty is set by the participating
agents rather than fixed at release. We introduce SWE-Duel, an arena in which two coding
agents evaluate each other over mirrored Red-Blue turns. In each turn, the Red
agent works on a real repository across three context-isolated sessions. It proposes and implements a new feature with feature tests, then embeds a subtle bug in that feature along with sealed bug-triggering tests. The resulting pull request is admitted only if (1) it passes the feature tests and the repository's existing tests while failing exactly the bug tests, and (2) a fresh session of the same
agent can repair the bug blind, which rules out unsolvable tasks. The Blue
agent then reviews the pull request without being told a bug exists. It wins only if its revision passes all three test suites, and then the two
agents swap roles. We run a tournament among 11 model-harness participants on 15 repositories in five
languages for 1,500 turns. The leaders win in different ways. GPT-5.5 with Codex CLI ranks first as the strongest solver, repairing 98.3% of the defects it reviews. Claude Opus 4.8 with mini-swe-
agent ranks second as the strongest task author, with a 27.3% Red win rate. Finally, within one model family, tasks written by the newest version defeat a fixed-reference solver far more often than those of older versions (42.9% vs. 4.0%), giving preliminary evidence that task difficulty scales with task authoring capability.
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All authors Wei Minn, Zhensu Sun, Lwin Khin Shar, David Lo
Computer Vision
MIRAGE: Metric Inference and Reliability-Aware Gaussian Estimation from Feed-Forward Vision
Xinzhe Wang, Changjian Jiang, Kaiwen Song et al. (+8 more)
Feed-forward 3D priors enable high-quality reconstruction from image streams but often lack reliable metric scale and suffer from long-term trajectory drift. In contrast,
LiDAR–inertial estimation provides metrically accurate motion but lacks the image-space consistency needed for high-fidelity reconstruction. We propose $MIRAGE$, which anchors feed-forward visual geometry to
LiDAR–IMU metric motion and regulates online pose corrections according to correspondence support and cross-modal agreement, jointly enabling accurate metric pose estimation and high-fidelity Gaussian reconstruction. Experiments on Oxford Spires, M2DGR and INS show that MIRAGE achieves accurate metric trajectory estimation, outperforming COLMAP, a widely used but computationally expensive reference for
vision-only
benchmarks. Motivated by the scarcity of long-horizon monocular
benchmarks for large-scale indoor reconstruction, we further build a
multimodal data-collection platform and use MIRAGE to establish accurate 6-DoF reference trajectories for a new
vision-only
benchmark. We
benchmark recent monocular large-scale reconstruction methods on this
dataset, providing a systematic evaluation of their performance in long-horizon indoor scenes.
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All authors Xinzhe Wang, Changjian Jiang, Kaiwen Song, Xudong Li, Kerui Ren, Ran Yi, Lizhuang Ma, Chunhua Shen, Linning Xu, Tao Lu, Mulin Yu
Robotics
CASTER: Formalizing Zero-Shot Manipulation from Synthetic Videos as Constrained Optimization
Yinlong Dai, Homanga Bharadhwaj and Dylan P. Losey
Prior research on learning from humans often requires real human demonstrations. But advances in
video generation models offer a new paradigm: we can now
prompt these generators to make synthetic
videos of the task. On the plus side, these synthetic
videos take place in the
robot's current environment, and do not require actual human teaching. However, while the generated
videos appear to show plausible motions, they lack both physical consistency and
robot grounding, and thus the
robot cannot directly roll out the trajectory shown by the
video. In this paper we seek to formalize the process of going from synthetic
video generation to real-world
robot trajectories for zero-shot manipulation tasks. We recognize that this problem is an instance of constrained optimization. The object trajectories extracted from the synthetic
video provide an initial guess at the correct motion, and the
robot must refine these trajectories (for instance, within a digital twin) while enforcing physical feasibility and semantic consistency. Our optimization-based formalism provides insights into the
video generation properties necessary for success. Specifically, we show that the
video generation must probabilistically seed the search within a semantic basin of attraction where the trajectory can be locally modified without losing its task
alignment. Our analysis further reveals that state-of-the-art approaches can be viewed as instances of our unified formalism with key simplifications. In our experiments we remove these simplifications to more closely match the underlying optimization, leading to more robust zero-shot trajectories from synthetic
videos and improving average task success by 34.1% compared to the best-performing baseline. See
videos at our project website: https://collab.me.vt.edu/CASTER
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Machine Learning
Sample-Adaptive Zeroth-Order Post-Training
Haiyuan Wan, Yanyi Li, Weicheng Lin et al. (+1 more)
Zeroth-order (ZO) optimization offers a
memory-efficient alternative to backpropagation for fine-tuning large
language models. However, existing ZO methods typically sample training examples uniformly, overlooking their evolving importance and leaving avoidable estimation variance. To address this limitation, we propose Sample-Adaptive Zeroth-Order Optimization (SAZO), which learns sample importance online and adaptively allocates queries to accelerate convergence. SAZO extracts gradient-norm information from existing directional differences to estimate sample importance without access to true gradients. Furthermore, to track evolving importance from sparse minibatch feedback, SAZO maintains per-example exponential moving averages that aggregate observations across updates. The method requires no additional model evaluations and only $O(N)$ additional scalar state compared with MeZO. We prove that SAZO asymptotically matches the leading convergence term of the minimum-variance sampling oracle, and achieves a strictly faster convergence rate than standard methods. Extensive experiments across four model families demonstrate improved average downstream performance and up to $6\times$ faster convergence to the same training loss in terms of query count.
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All authors Haiyuan Wan, Yanyi Li, Weicheng Lin, Cong Fang
Robotics
What Should Embodied Agents Remember? Benchmarking and Evaluating Memory Modality for Embodied Agents
Junxiang Wang, Yuchen Liu, Yi Wu et al. (+6 more)
Memory allows large
language model (LLM)
agents to use past experience, often stored as textual records, when responding to requests. For embodied
agents that control
robots, visual and spatial observations introduce a broader choice of
memory modalities, affecting both the information available for subsequent tasks and the model inference costs of constructing and using
memory. We introduce MeMoREval, a
benchmark for studying this problem through a controlled experience-request protocol.
Agents first experience a sequence of events without knowing the subsequent tasks, and subsequently they use
memory to respond to information and action requests without receiving new observations. The
benchmark comprises 53 tasks across four simulated indoor environments and measures task success alongside the inference costs of
memory construction and use. Our reference evaluations reveal that storing frames outperforms storing generated descriptions at lower write-time inference cost for the primary model, while storing frames with scene graphs achieves the highest aggregate task success for every tested model. Extensions to accumulated experience, selective frame retention, and visual input to
memory writers demonstrate how the
benchmark accommodates further
memory-design investigations. Two physical-
robot demonstrations establish the feasibility of applying its protocol beyond simulation.
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All authors Junxiang Wang, Yuchen Liu, Yi Wu, Guofei Chen, Nir Pechuk, Tao Chen, Zackory Erickson, Yuzhe Qin, Tianyu Li
Robotics
PanTact: Teaching Whole-Body Dexterity with a Wearable Contact Interface
Xiaomeng Xu, Han Zhang, Zhian Ruan et al. (+6 more)
Humanoid
robots have many degrees of freedom and contact surfaces, yet most loco-manipulation research restricts manipulation to the hands and avoids other body contacts with the environment. To extend manipulation beyond the hands, we present PanTact, a system that learns humanoid whole-body dexterity, the capability to manipulate with any suitable body surface, directly from
robot-free human demonstrations. Our key idea is to use contact as manipulation-centric information that transfers across embodiments. Contact locations and timing specify the interactions needed to accomplish a task, while the
robot chooses how to coordinate its body to realize them within its own physical constraints. Specifically, PanTact captures, learns, and executes whole-body contact via: 1) a wearable,
robot-free data collection interface that augments egocentric
vision and bimanual UMI grippers with whole-body modular contact patches to record body contact locations and states; 2) a visuomotor policy that mitigates the egocentric observation gap and predicts whole-body contact goals; and 3) contact-aware whole-body control formulations that realize active contact goals using suitable
robot surfaces. We deploy PanTact on a Unitree G1 humanoid without additional body contact sensors, demonstrating whole-body dexterity involving body support, pushing, sweeping, and load-sharing. Website: https://pantact.github.io
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All authors Xiaomeng Xu, Han Zhang, Zhian Ruan, Xiaofeng Guo, Jinkun Cao, Ruihan Yang, Carmelo Sferrazza, Shuran Song, Guanya Shi
Robotics
Fly-by-Code: Embodied Coding Agents for Aerial Manipulation with Active Visual and Physical Feedback
Jaewoo Lee, Jeongyeon Seo, Sihyun Cho et al. (+10 more)
Embodied coding
agents construct
robot policies from natural-
language instructions by generating executable programs and refining them from execution feedback. This refinement assumes that execution feedback reveals whether the task succeeded and, after a failure, provides sufficient evidence to guide program revision. Aerial manipulation violates this assumption in two ways. Payload constraints restrict onboard camera coverage and leave task-critical outcomes outside the
robot's view (partial observability), while strong dynamic coupling and limited control margins make physical interactions fragile (limited interaction robustness) and leave failure causes unresolved. Re-examining the same feedback cannot recover observations that were never collected or separate failure causes that the available evidence does not distinguish. To acquire this missing evidence, we introduce Active Feedback (AF), an active "View and Probe" framework. When execution feedback leaves unresolved questions, the
agent synthesizes a separate diagnostic program that runs in simulation to inspect unseen outcomes from informative viewpoints (View) or test hypotheses about failure causes through targeted physical interactions (Probe). Across six simulated aerial manipulation tasks with 60 trials per condition, AF reduces false completion declarations from 25 to 5 and raises final task success from 31.7% to 75% compared with terminal feedback under the same coding-
agent configuration and task-execution limit. Using shared
robot APIs, task programs refined with AF in simulation run on a physical aerial manipulator without task-program source-code modification and complete multi-stage manipulation tasks in the real world. These results show that executable diagnosis fills gaps in execution feedback for embodied coding
agents, improving both completion assessment and program refinement in aerial manipulation.
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All authors Jaewoo Lee, Jeongyeon Seo, Sihyun Cho, Gyeongrak Choe, Yutong Wang, Bavin Saravanan, Jia-Bin Huang, Furong Huang, Sebastian Scherer, Guanya Shi, H. Jin Kim, Seungjae Lee, Dongjae Lee
Computation and Language
AutoIndexer: Flexible-Order Decoding by Training Causal Models on Chains of Edits
Shu Ishida, Aliasghar Khani, Tianyu Zhang et al. (+3 more)
Autoregressive decoding is less suited to non-causally ordered data, e.g., cross-references or future dependencies, since their outputs cannot be revised post hoc, unlike iterative human
reasoning.
Diffusion LLMs partially mitigate this limitation but still cannot flexibly insert or remove arbitrary numbers of
tokens from a pre-generated context. We design AutoIndexer as a generative model with a novel training and decoding mechanism that simulates a Chain of Edits, allowing the model to flexibly modify its prior outputs in hindsight via
token insertions, substitutions, and deletions. The model learns to predict marker
tokens for opening and closing edits, simultaneously moving a cursor to positions in the existing
token sequence, analogous to a text editor. Training sequences are obtained by perturbing ground-truth labels with inverse chained edit operations, and the model learns to edit by undoing those inverses. The perturbation training allows AutoIndexer to be robust to erroneous context and adaptable to editing tasks such as code correction. We demonstrate that our method exhibits post-generation context-editing capabilities that most autoregressive and
diffusion models lack without compromising performance on standard LLM
benchmarks. The code is available at https://github.com/AutodeskAILab/autoindexer.
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All authors Shu Ishida, Aliasghar Khani, Tianyu Zhang, The Cong Luong, James Seale Smith, Adam Gaier
Machine Learning
Transformer-Based Masked Diffusion Models Provably Learn Distributions with Latent Structure
Jingda Wu and Changxiao Cai
Masked
diffusion models (MDMs) generate discrete data by iteratively unmasking multiple
tokens in parallel using a learned mask predictor. Their output carries two errors: parallelization error due to parallel sampling, and estimation error from the mask predictor learned with finite samples. Existing theory treats these errors separately, and the available statistical bounds typically scale with the number $V^L$ of length-$L$ sequences over a vocabulary of size $V$, suffering from the curse of dimensionality. In this work, we establish end-to-end statistical guarantees showing that
transformer-based MDMs learn structured distributions with a sample complexity governed by their latent structure. Specifically, we model this structure through mixtures of hidden Markov trees, which capture latent dependencies within components and heterogeneity across components. When the data distribution is well approximated by such a mixture, a
transformer-based MDM with parallel unmasking schedules achieves $\varepsilon$-accurate sampling in KL divergence with $\widetilde{O}(LK_{\mathrm{par}}/\varepsilon)$ training samples, where $K_{\mathrm{par}}$ is
polynomial in $L$, $V$, the number of hidden states, and the number of mixture components. The
transformer network and sampling procedure require no knowledge of the tree
topologies or component parameters, thereby adapting to the unknown latent structure automatically. Overall, our results provide theoretical justification for the empirical effectiveness of MDMs on real-world data.
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Artificial Intelligence
Equal-Budget Monte Carlo Tree Search versus Randomized-Restart List Scheduling for DAG Workflows: A Controlled Equal-Budget Computational Study
Yuyu Zhang
Monte Carlo Tree Search (MCTS) is often proposed for scheduling directed-acyclic-graph (DAG) workflows, yet most studies never run the comparison that could refute it - giving a much simpler method the same search effort. We run that comparison under a controlled protocol. All arms receive an identical budget - the same number of candidate schedules built and evaluated: an MCTS scheduling method, preregistered as the main arm; a randomized-restart list scheduler with no search tree as the control; and a textbook-UCT MCTS arm as a registered follow-up on the same domain. The analysis plan was locked before data collection; an independent checker confirmed all 768 confirmation-stage schedules valid with correct finish times. At the main budget of 2048 evaluated schedules, MCTS is 2.88% worse than the restarts (95% CI [-3.62%,-2.15%]; the 72 of 96 held-out instances with positive baseline-relative headroom). The deficit is not budget starvation: on the separate exploration pool restarts self-improve by 1.85% where MCTS gains 0.24%, and 700 of 720 of MCTS's first results are merely the warm start it was handed. The studied selection rule locks deterministically onto the first-expanded child, keeping its trials correlated while restarts draw gains from the tails; the causal link to makespan remains an inference. A structural map locates where the tree stops paying: restarts win on sparse irregular workflows, no difference is detectable on regular layered ones, and dense ones leave no room to improve. Swapping the rule for textbook UCT removes most of the deficit (-0.26%, 95% CI [-0.71%,+0.23%]) without reversing its sign. Results are confined to a controlled synthetic domain (N in {8,16,32} tasks, 2-8 processors, budgets up to 2048); we make no claim about larger budgets.
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Machine Learning
Enriched Holomorphic Neural Networks for 2D and 3D Laplace Problems
Kalp Shah, Andrei Vlad Nicula, Mihnea Andrei Bârsan et al. (+4 more)
Boundary-value problems for the Laplace equation develop singular behaviour near corners, edges and junctions that smooth approximators resolve poorly. We combine holomorphic neural networks with singular functions derived from the local geometry and boundary conditions. The representation is harmonic by construction, so no interior residual is needed: the network fits the smooth remainder from boundary data alone, and the enrichment terms capture the singular behaviour. Singular exponents are trainable, so the construction also applies when the local expansion is not known in advance. We extend the approach to three dimensions using independent networks evaluated along complex null directions. The enriched model is the most accurate tested method on every problem. Its margin is several orders of magnitude where the enrichment captures the singularity exactly, and a factor of about 1.7-2.1 over the next-best method on a CdZnTe radiation detector with many singular sites, in both two and three dimensions.
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All authors Kalp Shah, Andrei Vlad Nicula, Mihnea Andrei Bârsan, Robert-Stefan Sofroni, Agneya Bhardwaj, Jan Jelínek, Alejandro Valverde Mahou
Cryptography and Security
Organizational Context for the Enterprise Security Brain
Eden Yavin, Ariel Zadok, Gal Engelberg et al. (+2 more)
Agents that investigate and decide on enterprise security reason over a machine-readable view of the environment: its assets, identities, configurations, relations, and findings. The correct judgment about an asset also depends on what the organization knows about it: who owns it, what business function it serves, which policy governs it, and which risks were accepted. Part of this knowledge can be inferred from machine state, such as an ownership tag; most of it is recorded by people in policies, audit reports, architecture diagrams, tickets, and conversations, outside machine state. We call this distance the organizational context gap. Closing it poses four challenges: extracting the right claims from heterogeneous organizational data and, at inference time, detecting claims made stale by live state, checking live state against the governing policy, and binding architecture diagrams, which carry the business logic, to live resources. We introduce organizational context as a layer of the enterprise security
brain and define it with a taxonomy and six properties its representation must preserve. We then define a context model in which a claim carries one meaning, an attestation records each source that asserts it, strength is derived from live evidence at read time, and claims resolve onto the entities of the asset graph. On a production environment spanning AWS, Okta, Google Workspace, and GitHub, extraction recovers 0.80 of a pooled 1,355-claim ground truth at 0.87 precision, with 0.5% of extracted claims unsupported by their source. An
agent reasoning over live state and resolved context raises the conflict in 0.97 of stale-finding tasks, and reaches answer correctness of 0.83 on policy-
alignment tasks and 0.86 on architecture tasks.
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All authors Eden Yavin, Ariel Zadok, Gal Engelberg, Elad Elouz, Leon Goldberg
Sound
Input Representation, Not Model Size or Pretraining Scale, Shapes Speaker Verification Robustness to Temporal Degradation
Erdem Baha Topbas, Srikanth Madikeri, Elisa Pellegrino et al. (+1 more)
Speech remains intelligible to listeners under heavy temporal degradation, but whether speaker identity survives is largely unexplored. We evaluate six automatic speaker verification (ASV) models, trained on temporally intact
speech, and tested under three degradations that remove different combinations of temporal cues. The models form three pairs, one per architecture family (convolutional,
transformer, conformer), each pairing a waveform-input with a mel filterbank-input model. All models degrade, but robustness tracks input representation rather than pretraining scale or model size. In all nine family-by-condition comparisons the filterbank-input model degrades less, with eight of nine 95% confidence intervals excluding zero, even when it is smaller or has less pretraining than its waveform counterpart. In the controlled convolutional pair, where the models differ only in input representation, the filterbank-input model's equal error rate rises from 1.1% to 17.1% under the heaviest tested degradation, against 2.9% to29.7% for its waveform counterpart.
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All authors Erdem Baha Topbas, Srikanth Madikeri, Elisa Pellegrino, Volker Dellwo
Machine Learning
Efficient Learning of Survival Analysis Models Under Outcome-Dependent Truncation and Censoring
Itai Zilberstein, Ioannis Anagnostides, Arman Kilic et al. (+1 more)
Modern machine learning models are typically trained on data assumed to be representative of the predictive tasks the model will face in the future, but practical
datasets violate this assumption. We focus on outcome-dependent truncation in which an observation appears in the training data depending on its label. Our work connects two largely distinct literatures: efficient learning under truncation and survival analysis. Existing methods for truncated survival analysis do not characterize the optimization landscape and instead rely on iterative procedures such as expectation maximization. We address this gap and prove that Cox regression can be efficiently learned under truncated and right-censored data using first-order optimization when the baseline hazard is known (despite nonconvexity). In contrast, when the baseline hazard is learned jointly, we construct identifiable and correctly specified models whose negative log-likelihood contains strict suboptimal local minima. On experiments with four survival
datasets, we show that accounting for truncation reduces the prediction bias induced by naive Cox regression and extends to neural networks. We also show orders of magnitude speedups compared to standard expectation maximization.
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All authors Itai Zilberstein, Ioannis Anagnostides, Arman Kilic, Tuomas Sandholm
Machine Learning
Market Impact Estimation via Causal Foundation Models
Dennis Thumm and Ying Chen
When a trader executes a large order, the relevant question is not what will the price be? but what would the price have been had I not traded?
Market impact is thus a causal quantity, yet classical models (Almgren–Chriss, propagators) regress price on flow, which is biased whenever informed flow moves prices through information as well as trading. We present a proof-of-concept Causal
Market Simulator: a continuous-time causal prior-data fitted network (PFN) trained on a synthetic
market prior in which a hidden informational flow confounds the flow–price relation by construction, with confounding strength an exact per-episode knob. On this prior, the naive
estimator's bias follows a closed form we confirm empirically (up to 68% overestimation), while the PFN tracks the unbiased true-parameter oracle at high confounding. Its do-ablated twin, the same model with the intervention inputs removed, collapses onto the pre-window mean. Zero-shot, the model responds at the ground-truth scale to metaorders in an ABIDES limit-order book ($+0.38$ pre-onset standard deviations against seed-replay counterfactuals), and its intervention response replicates on about 2,300 detected flow bursts in BTC/ETH/SOL perpetuals and 3,800 on Nasdaq equities, evidence consistent with, though not proof of, causal transfer. Off-prior the response degrades gracefully, and split-conformal recalibration repairs interval calibration. Transfer hinges on encoding the metaorder as added flow rather than a clamped price level, not on more training. A recurrent state-space variant closes the remaining shape gap: its impact accrues over execution duration where the
transformer's stays flat, raising the correlation with measured metaorder impact from $+0.68$ to between $+0.92$ and $+0.96$, at the cost of weaker on-prior deconfounding that traces to the training recipe rather than to recurrence.
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Artificial Intelligence
EliteTable: Self-Improving Program Search for Executable Control Policies
Stuart Farmer
EliteTable is a self-improving program-search
algorithm guided by large
language models (LLMs) that uses a fixed-capacity table of high-scoring solutions to guide new proposals, edits of existing solutions, and remixes of several solutions. Performance was evaluated across six Gymnasium control tasks, each with ten generations and fifty candidate slots per generation. Each task was evaluated across ten independently initialized searches. Candidate-score medians rose between the first and final generations in all six tasks, and selected-winner search-score medians rose in five. In CartPole-v1, the selected winner reached the search-score ceiling in the first generation of all ten searches. All ten paired held-out endpoints improved in four of the six tasks. BipedalWalker-v3 and CarRacing-v3 improved without reaching their reporting targets. Six of ten Acrobot-v1 searches declined on held-out episodes, and the median paired change was negative. The results document improvement during program search and the transfer of selected-winner gains to new episodes in some tasks. Comparisons and ablations are needed to establish EliteTable's relative performance and the contributions of its components.
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Cryptography and Security
SoK: Privacy-Preserving Order Matching
Prerna Arote, Adrian Saiz and Lucianna Kiffer
Privacy-preserving order matching enables traders to discover and execute compatible orders while limiting disclosure of sensitive trading information. Existing systems span financial
markets and blockchain-based asset exchanges and employ diverse
market structures,
privacy mechanisms, and trust assumptions, making their
privacy guarantees difficult to compare. We present a systematization of
privacy-preserving order matching covering 22 protocol families. We organize the order lifecycle into four phases: order representation and submission, order discovery and collection, order-matching, and settlement, and characterize each phase by protected and exposed information, access roles,
privacy mechanisms, and trust assumptions. Our analysis shows that
privacy is not a single protocol-level property: protecting selected order attributes or individual phases does not necessarily prevent leakage elsewhere in the lifecycle. We identify recurring trade-offs among
privacy, functionality, trust, and verifiability, highlighting the challenge of achieving end-to-end
privacy across the complete order lifecycle.
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Sound
Structural and Representational Agency in LLM-Driven Timbre Generation and Interpolation
Joseph Cameron and Alan Blackwell
Generative creative interfaces routinely report the user's position within a space of possibilities. Such displays imply that the quantities shown were measured from the artefact; in a growing class of systems they are instead predicted by a
language model describing its own output, and the two are indistinguishable on screen. Most creative domains lack an agreed measurement against which such a self-report can be checked; audio admits one. In a measurement study of a working LLM-driven timbre interface, what a user can cause proves separable from what the interface reports. The first is exactly characterisable: the reachable set is a zonotope whose dimension increases by one per sound placed, so the space the model may act within is demonstrably the user's own. The second is inaccurate. The displayed position differs from measurement by approximately three times the width of that region, and the error has two independent sources: model-predicted coordinates, whose accuracy varies twofold between the two models evaluated, and the interpolation step, which arises from combining synthesiser parameters and is unaffected by model quality. End to end, 26 of 36 refinement instructions produced no perceptible change, and under the weaker model the facility performed no action while reporting convergence. Effective agency (what a user can cause) and epistemic agency (whether they can know it) are therefore separable and independently engineered: structurally enforced constraints survive model failure, whereas guarantees resting on the model's own report do not. The failure mode that remains is silence presented as success. We close with a short protocol for building and auditing such displays.
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Machine Learning
CacheScope: Exact-Compute Allocation for Training-Free Diffusion Acceleration
Qinhan Lyu, Jusheng Zhang, Zhixing Zhang et al. (+4 more)
Training-free feature reuse accelerates
diffusion transformer inference by replacing full denoising computation at selected steps with cached approximations. Existing methods mainly improve the reconstruction of skipped features, but we find that once a stable first-order reconstructor is used, increasing reconstruction complexity brings limited gains, whereas the placement of exact-computation steps has a much larger effect on fidelity. More importantly, absolute shallow-probe variation is jointly affected by input-trajectory displacement and network response strength, and therefore does not directly reflect cache risk. We address this confounding by normalizing probe-output variation with the corresponding input displacement, yielding a directional gain per unit trajectory movement. Based on this signal, we propose CacheScope, which identifies high-risk reuse steps online and selectively falls back to full denoising computation while leaving the underlying cache reconstructor unchanged. CacheScope requires no additional training, gradients, auxiliary networks, or
prompt-specific offline schedule optimization. Across FLUX.1-dev, Wan2.1-T2V-1.3B, and Open-Sora 1.2, CacheScope achieves up to 3.57x acceleration and reduces median LPIPS by up to 70.1% over the strongest probe-based baseline.
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All authors Qinhan Lyu, Jusheng Zhang, Zhixing Zhang, Hao Liu, Zijie Liang, Jin Jiang, Keze Wang
Software Engineering
Fix What's Wrong, Keep What's Right: Hallucination Repair for LLM-Based Optimization Modeling
Jiageng Wu, Chao Shen, Zihan Guo et al. (+5 more)
Large
language models (LLMs) are increasingly being explored to automate operations research modeling, reduce modeling time and lower the expertise barrier to optimization based decision support. However, this process often introduces inconsistencies between generated artifacts and the original problem, which we refer to as optimization modeling
hallucinations. Despite progress in general-purpose
hallucination mitigation, optimization modeling poses a distinct repair challenge: restoring fidelity to the original problem through coordinated model-code updates. We introduce OPT-Kraken, a training-free
hallucination repair framework. To the best of our knowledge, it is the first post-hoc framework for repairing
hallucinations in LLM-based optimization modeling artifacts. It extracts problem requirements and instance data, localizes errors by checking problem-model fidelity, model-code consistency, and data grounding, and selects repair granularity by the affected scope. For local repair, category rules and instance evidence are compiled into contracts specifying permitted edits, expected effects, and protected content. Experiments on established optimization modeling
benchmarks show positive gains across automatic modeling frameworks and fine-tuned models. On zero-shot artifacts from multiple base models, OPT-Kraken effectively repairs optimization modeling
hallucinations while preserving initially correct artifacts. These results demonstrate effective and controlled repair for LLM-based optimization modeling artifacts.
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All authors Jiageng Wu, Chao Shen, Zihan Guo, Junyan Shao, Zongyan Zhang, Jie Song, Zhong Li, Mingyang Sun
Machine Learning
GlucoPRISM: Protocol-Supervised Pretraining for Cross-Cohort Glucose Transfer
Adinath Madhavrao Dukre, Abdullah Mohammad, Yifan Lu et al. (+3 more)
Continuous glucose monitoring (CGM) foundation models encode each day as one vector that mixes who the person is, what happened that day and how the sensor measured it, so measurement differences travel with the representation into new cohorts. We ask whether pretraining can place the measurement component that harms transfer in known coordinates, so that deployment drops them with a fixed projection and no target data. GlucoPRISM supervises a GlucoFM backbone with response contracts, rules for how each part of the representation should change when only the day or only the sensor changes: a 64-dimensional Trait band should change with neither, a 48-dimensional State band only across days, and a 16-dimensional Sensor band should track the device. Synthetic sensor views calibrated on paired Dexcom and Libre recordings, different-day pairs and glucose-summary targets supply this supervision without clinical labels. Across twelve cross-cohort directions, dropping the Sensor band from the same frozen encoder raises mean transfer AUROC from 68.75 to 73.57, against 69.3 for GlucoFM. The gain has two parts. Into CGMacros, the one target cohort recorded with two sensor types, filtering adds 11 to 19 points, and this holds when the generator is recalibrated on half of the participants and scored on the other half. On the other eight directions filtering is neutral and the encoder itself exceeds GlucoFM by 3.5 points. The device remains linearly decodable after filtering, so the filter removes a device-related component that harms transfer rather than all measurement information. The retained bands improve window AUROC on thirteen of fourteen public task–cohort
cells (Holm-adjusted p=0.031) and correlation on fourteen of fifteen clinical endpoints in an external cohort of 9,303 participants.
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All authors Adinath Madhavrao Dukre, Abdullah Mohammad, Yifan Lu, Eran Segal, Ahmed Metwally, Imran Razzak
Machine Learning
Behind-the-Meter Photovoltaic Visibility from Net Smart Meters: Deployment Regimes and Operational Insights
Jack Zheng and Hao Wang
Distribution operators need visibility of behind-the-meter photovoltaic (PV) generation, yet most residential customers are observed only through net smart meters. Deep-learning-based PV disaggregation can restore this visibility, but its operational value depends on whether the model serves known customers, new customers, future periods, or new regions. We quantify this dependence using four deployment regimes that separate household transfer from temporal extrapolation, applied to Ausgrid smart-meter data with cross-
dataset adaptation to the Pecan Street data. Using a fixed training protocol, we
benchmark representative deep sequence architectures for PV disaggregation under regime-matched evaluation. At a fixed 48-hour context length, daylight mean absolute error (MAE) of the best model rises from 0.104~kW on seen households in the future test period to 0.185~kW on unseen households over the same period, and under a fixed training contract the deployment regime explains far more of the error variation than model choice. Under target-domain normalization, fine-tuning on five target-region households reduces source-model transfer error by 28%–36%. Long input windows help known-customer extrapolation, while their new-customer benefits vary by model. These findings suggest regime-matched error budgets, limited target supervision for model adaptation in new regions, and use-case-dependent context selection for energy data analytics.
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Cryptography and Security
An Exploration of Attacks on AI Agent Systems
Cathleen Qiao, Layah Vigneaud, Chloe Brandon et al. (+9 more)
As autonomous AI
agents become more integrated into sensitive domains, it is crucial that single and multi-
agent AI systems are secured. This paper investigates how
prompt injection attacks can exploit both direct user inputs and inter?
agent communication to leak sensitive information and trigger cross-
agent infections. While our tests mostly highlight key vulnerabilities in single-
agent systems, we also extend these attacks to multi-
agent frameworks, investigating how weaknesses can propagate across
agents. Our findings emphasize the need for more robust defenses as AI
agents become more commonly embedded in real-world workflows.
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All authors Cathleen Qiao, Layah Vigneaud, Chloe Brandon, Max Cabilangan, Xiyuan Cao, Isaac Khabra, Reva Mahajan, Lena Nguyen, Kyle Pak, Anagh Pandya, Roshni Dave, Peiran Wang
Machine Learning · Cross-listed from physics.chem-ph
MolP2SBench: Do LLMs Really Reason from Molecular Properties to Structure?
Yuhang Xie, Zhiyi Shi, Zeyi Zhang et al. (+4 more)
Large
language models (LLMs) are increasingly used in molecular discovery and structure elucidation, where decisions depend on physical observations. Existing evaluations primarily focus on end-to-end molecular reconstruction, where correct predictions do not necessarily indicate reliance on the supplied properties. We introduce MolP2SBench, a
benchmark for molecular property-to-structure (P2S)
reasoning in LLMs. Each task presents a masked molecular scaffold with given properties and asks the model to select the missing structure from plausible candidates. We shuffle or replace properties to test reliance on correct property-structure pairing and responsiveness to counterfactual property changes. A channel-wise leaderboard uses Property-Grounded Recovery (PGR), giving equal weight to source and counterfactual recovery. Evaluating seven models, we find that LLMs can use physical properties for structure prediction, but performance varies substantially across property types. Higher accuracy with a property does not necessarily imply greater responsiveness to it: a model may benefit more from $^1$H NMR than from $^{13}$C NMR, yet be less likely to change its prediction when the $^1$H data is replaced. LLM performance also need not reflect the relative difficulty of these properties for human chemists. Although UV-Vis spectra provide less direct structural information than NMR spectra, Astra achieves higher accuracy with UV-Vis data than with NMR peaks. MolP2SBench supports studying how LLMs extract and use physical-property information, revealing patterns that may differ from conventional chemical intuition and providing a foundation for more reliably property-grounded models for molecular and
quantum-chemical inference. Code and
dataset: https://github.com/DUAL-Xiao/MolP2SBench.
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All authors Yuhang Xie, Zhiyi Shi, Zeyi Zhang, Zhisheng Qi, Yushun Dong, Yu Wang, Xiongye Xiao
Robotics
Dex-One2Many: Learning Dexterous Manipulation from a Single Human Demonstration
Jusuk Lee, Sungha Kim, Yeonsoo Park et al. (+8 more)
While learning dexterous manipulation from a single human
video offers a promising alternative to costly
robot demonstrations, many recent methods predominantly imitate demonstrated motions. Such strict motion matching often limits generalization to initial object poses, goal poses, and grasps not shown in the
video. Alternatively, discovering a policy via reinforcement learning (RL) allows for broad generalization, but without prior guidance, it struggles with high-dimensional exploration in complex, multi-stage tasks. To address these coupled generalization and exploration challenges, we present Dex-One2Many, a real-to-sim-to-real framework that learns a generalizable dexterous manipulation policy from a single human
video. Our key insight is to abstract the
video into sequential scene graphs that guide RL, enabling efficient exploration while preserving broad generalizability. The graphs serve as generative constraints for sampling diverse reset states and provide dense rewards for each stage. Because the graphs constrain relations rather than exact poses, these reset states cover object poses and grasps beyond the
video, while initializing each stage from them with dense rewards keeps exploration short and guided. Trained entirely in simulation, Dex-One2Many transfers zero-shot to a real multi-fingered hand. Across five tool-use and manipulation tasks, Dex-One2Many exceeds baselines by 6.5% in seen configurations, while its robust generalization widens this gap to 71% in unseen scenarios.
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All authors Jusuk Lee, Sungha Kim, Yeonsoo Park, Jonguk Cheon, Yoonkyo Jung, Yongjun You, H. Jin Kim, Jia-Bin Huang, Furong Huang, Youngseok Jang, Seungjae Lee
Computer Vision
Rubric-CEPR: Self-Evolving Image Editing via Reward-Verified Self-Distillation
Ritesh Thawkar, Shubham Patle, Shravan Venkatraman et al. (+1 more)
Instruction-guided image editors have become highly capable, yet improving them further still depends on human-edited training pairs or external reward models. Such supervision is costly to obtain and can reward plausible failures: a realistic output may leave the requested change undone or alter content that should be preserved. In this work, we strive to improve a pretrained image editor using only its own generations, without human-edited targets or an external training-time reward model. To this end, we propose a self-evolving framework, named Rubric-CEPR, that verifies the editor's own samples with its internal representations through a rubric-augmented Contrastive Edit-Preservation Reward (CEPR). A Planner proposes structured edit instructions from unlabeled images, the Editor samples multiple candidate edits, and a frozen Critic scores each candidate with decomposed rubric checks for edit realization, removal of the old state, and content preservation, using features already exposed by the editor. Non-compensatory gates reject infeasible candidates, and the best verified candidate is distilled into the editor through lightweight adapter training. On Qwen-Image-Edit, Rubric-CEPR improves ImgEdit from 4.36 to 4.60 (+5.5%), with a +24.9% gain on object isolation, and transfers to GEdit-Bench and Complex-Edit. The same procedure also improves Step1X-Edit by +7.8% on ImgEdit. We hope our approach will serve as a solid baseline for image editors that improve themselves from their own verified samples. Our code is publicly available at https://github.com/riteshthawkar/Rubric-CEPR
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All authors Ritesh Thawkar, Shubham Patle, Shravan Venkatraman, Rao Muhammad Anwer