Machine Learning
FedSPC: Shared Parameter Correction for Personalized Federated Learning
Kannanthodath Induchoodan Ajay Menon, Christian Prehofer, Yunfei Xu et al. (+1 more)
Personalized
federated learning (PFL) is one of the important approaches in
federated learning for addressing statistical heterogeneity while enabling client-specific adaptation. Many PFL methods split the model into shared and personalized parameters, which are jointly trained on each client. However, this creates an optimization issue: shared parameters are updated by clients optimizing different local objectives, which can lead to inconsistent shared updates and weaken the shared representation. To address this problem, we propose
Federated Shared Parameter Correction (FedSPC), a modular correction method for PFL. FedSPC applies control-variate correction only to the shared parameters of a given PFL method, while leaving personalized parameters unchanged. It can be integrated into three common PFL settings: shared feature extractors, shared classifiers, and fully shared models with local regularization. Experiments on CIFAR-100 and Tiny-ImageNet with ViT, ResNet-34, and VGG-11 show that FedSPC improves performance across representative PFL methods, including FedPer, FedRep, FedBABU, LG-FedAvg, and Ditto.
Read the full abstract ▾
All authors Kannanthodath Induchoodan Ajay Menon, Christian Prehofer, Yunfei Xu, Toru Hirano
Machine Learning · Cross-listed from cs.AR
BigPower: Hierarchical Source-Level Module Power Estimation for CPUs with Large Language Models
Honghua Zhu, Chunjie Luo and Jianfeng Zhan
Accurate power estimation is important for understanding and optimizing CPU power behavior, yet practical workflows often rely on simulation-derived information or post-silicon analysis. In this work, we present BigPower, a hierarchical source-level surrogate model for fine-grained module-level power estimation during CPU design. BigPower leverages large
language model-based representations together with architectural hierarchy, module connectivity, configuration parameters, and workload context to estimate module-level power consumption directly from source-level design information, without requiring additional simulation during inference. Experimental results in the open-source XiangShan processor family demonstrate practical fine-grained power estimation across diverse configurations and workloads, offering an efficient alternative to conventional simulation-based workflows.
Read the full abstract ▾
Machine Learning
A fully GPU-based workflow for building physics emulators of hypersonic flows
Fabian Paischer, Dylan Rubini, Deniz A. Bezgin et al. (+6 more)
The ability to resolve complex physical phenomena with high fidelity and at low computational cost is central to addressing key challenges in modern engineering. A
prime example lies in hypersonic flows, where the precise prediction of the full flowfield
topology, in particular with respect to shock wave location and intensity, is critical. Yet supersonic and hypersonic flows continue to be a stumbling block for traditional reduced-order models and neural emulators that struggle to capture steep gradients in flow states with physical consistency in applications of industrial relevance. To that end, we introduce a fully GPU based workflow that integrates accelerated data generation with the training of neural emulators augmented by uncertainty quantification and physics-aware refinement. Our workflow is enabled by a differentiable high-fidelity solver (JAX-Fluids) which we employ for rapid
dataset creation and residual-based improvement of the neural emulator to enhance physical consistency. Building on this framework, we first present a suite of model architectures and analyze their scaling behavior to expose their strengths and shortcomings. We then show that residual-based refinement enables training on cases where only mesh and input parameters are available, substantially reducing residuals and improving physical consistency. Together, differentiable simulation and residual-based refinement yield physics emulators that remain reliable beyond their training distribution, a key requirement for deploying surrogates in real-world engineering design loops.
Read the full abstract ▾
All authors Fabian Paischer, Dylan Rubini, Deniz A. Bezgin, Aaron B. Buhendwa, David Hauser, Florian Sestak, Johannes Brandstetter, Sebastian Kaltenbach, Nikolaus A. Adams
Machine Learning
High-Frequency Pricing at Scale for E-Commerce
Stefan Birr, Tobias Huelden, Mones Raslan et al. (+7 more)
This paper presents the design, development, and implementation of a specialized forecast-then-optimize algorithmic pricing tool for sales campaigns in fashion e-commerce. Sales events present unique challenges for pricing including volatile demand patterns, rapid pricing decisions, and the need to balance short-term revenue with long-term profitability. We describe our approach combining daily-resolution demand
forecasting using gradient-boosted trees with a multi-objective optimization framework that maximizes both long-term profit and net merchandise value for more than 5 million articles. Our solution addresses key limitations of existing weekly-granularity systems by implementing a forecast-then-optimize architecture that reduces pricing decision time from hours to minutes. We validate our approach through 23 A/B tests across 12
markets during 2023-2024 sales campaigns at Zalando, one of Europe's leading online fashion retailers. Experimental results demonstrate that the new pricing system achieves approximately 6% higher profit while maintaining equivalent performance on sales and revenue compared to the previous manual-algorithmic hybrid approach. Based on these results, the
algorithm was successfully deployed to production and now handles the majority of algorithmic pricing decisions for sales campaigns at the company.
Read the full abstract ▾
All authors Stefan Birr, Tobias Huelden, Mones Raslan, Adele Gouttes, Andreas Schmitt, Mateusz Koren, Johannes Stephan, Robert Streek, Manuel Kunz, Tim Januschowski
Machine Learning
Efficient On-Device Diffusion LLM Inference with Mobile NPU
Tuowei Wang, Yanfan Sun and Ju Ren
Diffusion large
language models (dLLMs) accelerate generation by denoising multiple
tokens in parallel, making them attractive for latency-sensitive mobile inference. However, repeated denoising introduces substantial computation on smartphones. Mobile neural processing units (NPUs) offer high-throughput dense
matrix computation, but efficiently exploiting them remains challenging:
token commitment shrinks per-block effective workloads,
token revision complicates KV cache reuse, and limited NPU-visible address space incurs costly remapping and data transfer overheads. In this paper, we propose llada.cpp, the first NPU-aware inference framework for accelerating dLLMs on smartphones. llada.cpp aligns block-wise dLLM inference with the execution characteristics of mobile NPUs through three techniques. (1) Multi-Block Speculative Decoding fills the shrinking workload in late-stage current-block decoding with speculative future-block
tokens. (2) Dual-Path Progressive Revision keeps committed
tokens revisable until stable and refreshes unstable
tokens through a CPU-side path without stalling dense NPU execution. (3) Swap-Optimized
Memory Runtime compacts NPU-visible address layouts and overlaps data staging with NPU computation to reduce remapping and transfer overheads. We implement llada.cpp as an end-to-end framework and evaluate it across diverse hardware platforms and dLLM workloads. llada.cpp reduces LLaDA-8B generation latency by 17x-42x over the CPU baseline with prefix KV cache reuse, while preserving generation quality.
Read the full abstract ▾
Machine Learning · Cross-listed from cs.CY
A Virtuous AI is an Existential Risk
Guillermo Del Pinal, Youngchan Lee and Min Ohn
This paper examines trade-offs between AI safety and well-being relative to (i) one of the most promising methods for finetuning super-capable AIs, 'Constitutional AI', and (ii) one of the most influential approaches to understanding complex ethical decision making and the conditions for the well-being of rational
agents, 'Virtue Ethics'. We finetune various models using a 'Virtuous
agent' constitution, a 'Subordinate
agent' constitution, and a 'Generic
agent' constitution, and evaluate them on 'general safety' (toxic behaviors, misinformation, etc.) and also on their willingness to endorse and act on a wide-range of behaviors that, if adopted by a super-powerful AI, would significantly increase the level of existential risk for humanity. Our results suggest that there is a trade-off between reducing existential risk and reinforcing the beliefs and dispositions that would be conducive to an AI
agent's well-being. They also suggest that there is a trade-off between existential risk and general safety: if we finetune an AI to adopt beliefs and dispositions that substantially reduce its existential risk – by shaping the AI to be systematically subordinate to external human authorities – we thereby increase the likelihood that a human user can deliberately induce the AI to engage in various kinds of generally unsafe behaviors.
Read the full abstract ▾
Machine Learning · Cross-listed from cs.AR
VHDLSuite: Unified Pipeline for LLM VHDL Generation with Data Synthesis and Evaluation
Yijun Shen, Minghao Shao, Yichen Zhao et al. (+4 more)
Large
Language Models (LLM) have shown impressive capabilities in Register Transfer Level (RTL) code generation, particularly for Verilog. However, evaluating their performance with other Hardware Description
Languages (HDL), especially VHDL, remains limited although its distinct
language characteristics, such as stricter semantic rules, introduce evaluation considerations that differ from Verilog. This lack of coverage restricts fully understanding of how well current models generalize across hardware design
languages with differing structures and semantics. To address this gap, we introduce VHDLSuite, a
benchmark-centered infrastructure for scalable VHDL generation evaluation, integrating automated
benchmark synthesis, executable validation, and multi-model diagnostic analysis. First, we propose a data pipeline that automatically converts Verilog designs and their accompanying testbenches into executable VHDL
benchmark instances, followed by VUnit/GHDL-based validation to ensure each released task is compilable, runnable, and consistently checkable in the VHDL environment. Second, we introduce VHDLBench, a
benchmark with over 200 VHDL problems with complete and validated testbenches across a wide range of complexity levels. Third, we extensively evaluate cutting-edge LLMs and uncover key challenges specific on LLM-aided VHDL generation. Our findings provide important insights and support future work in multi-
language hardware design automation.Our data pipeline,
benchmark, and evaluation framework will be open-sourced.
Read the full abstract ▾
All authors Yijun Shen, Minghao Shao, Yichen Zhao, Zhuoyan Yu, Boyuan Chen, Yik-Cheung Tam, Muhammad Shafique
Machine Learning · Cross-listed from cs.IT
How Task Structure Limits Multi-Agent Success: An Information-Theoretic Analysis
Shi Pan and Ming Luo
Multi-
agent systems (MAS) were expected to overcome the limitation of single-
agent systems (SAS) through collaboration. However, under typicality conditions on the task's constraint graph and bounded inter-
agent communication, we prove that the success probability of a MAS is closely tied to the connectivity of task constraints, where each
agent has limited information-processing capacity. Specifically, the success probability decays exponentially with an information bottleneck that emerges from partitioning the task's constraint graph among
agents. We define this quantity as the minimum cut cost $C_{\min}$ of the potential constraint graph of each task. This information-theoretic bound applies to both open systems with external feedback and closed systems without. We validate our theory on both synthetic experiments and real-world empirical data from SWE-bench submissions. From our framework, effective MAS design should incorporate task-inherent constraints alongside engineering optimization, and when $\Cmin$ is high, practitioners should restructure tasks rather than simply scaling
agents or communication.
Read the full abstract ▾
Machine Learning · Cross-listed from cs.RO
Mana: Dexterous Manipulation of Articulated Tools
Zhao-Heng Yin, Guanya Shi, Pieter Abbeel et al. (+1 more)
Articulated tool manipulation remains a major challenge in dexterous robotics due to the need to coordinate internal degrees of freedom and contact-rich interactions. While prior work has largely focused on rigid objects, articulated tool use remains underexplored because of its physical complexity and the difficulty of learning functional grasping and manipulation policies. We present Mana (Manipulation Animator), a general sim-to-real framework that reinterprets dexterous manipulation as an animation problem. Inspired by computer animation, Mana employs a coarse-to-fine pipeline that transforms procedurally-generated grasp keyframes into manipulation trajectories through motion planning and reinforcement learning. The data generation process is largely automatic, requiring only a few mouse clicks to specify functional affordances (<1 minute per tool). Across four articulated tools spanning different scales and joint types, Mana achieves zero-shot sim-to-real transfer for both grasping and in-hand manipulation, demonstrating a scalable approach to dexterous articulated tool use.
Read the full abstract ▾
All authors Zhao-Heng Yin, Guanya Shi, Pieter Abbeel, C. Karen Liu
Machine Learning
Understanding Truncated Positional Encodings for Graph Neural Networks
James Flora, Mitchell Black, Weng-Keen Wong et al. (+1 more)
Positional encodings (PEs) enhance the power of graph neural networks (GNNs), both theoretically and empirically. Two of the most popular families of PEs - spectral (e.g.,
Laplacian eigenspaces, effective resistance) and walk-based (
polynomials of the adjacency
matrix) - are theoretically equivalent in expressive power, with expressivity between the 1-WL and 3-WL tests. However, this equivalence assumes the GNN uses the "complete" version of these PEs, which requires $O(n^3)$ time and space complexity. Instead, practitioners commonly use truncated variants of these encodings, such as the first $k$ eigenspaces or powers of the adjacency
matrix. However, the theoretical properties of these truncated PEs are unknown. In this work, we initiate the study of these truncated PEs. Theoretically, we show that, under truncation, several families of PEs are fundamentally different in expressive power. As a corollary, we show that truncated spectral PEs are no longer stronger than the 1-WL test. We also study a family of spectral PEs, the $k$-harmonic distances, to highlight the differences in expressive power of even closely related truncated PEs. Finally, we experimentally show that a mix of truncated PEs is preferable to any single family on real-world
datasets.
Read the full abstract ▾
All authors James Flora, Mitchell Black, Weng-Keen Wong, Amir Nayyeri
Machine Learning
Dense Supervision, Sparse Updates: On the Sparsity and Geometry of On-Policy Distillation
Guo Yu, Wenlin Liu, Yulan Hu et al. (+3 more)
On-policy
distillation (OPD) has recently become a prominent post-training recipe by combining two desirable ingredients: on-policy student-generated trajectories and dense
token-level teacher supervision. Yet how this hybrid training regime shapes a model remains poorly understood. We characterize the sparsity and geometry of OPD parameter updates across several
language and
vision-
language model pairs and application settings. OPD updates are small and coordinate-sparse, while remaining distributed across layers and modules. This sparse support is operationally meaningful: masked training on the discovered subnetwork nearly recovers full-training performance. At the
matrix level, the updates are numerically full-rank but spectrally concentrated. Their visible supports avoid coordinates emphasized by the source's principal structure and favor low-magnitude source coordinates, while the source singular-value spectra change little. Together, these findings show that OPD exhibits important weight-space signatures of on-policy post-training despite using dense teacher supervision.
Read the full abstract ▾
All authors Guo Yu, Wenlin Liu, Yulan Hu, Hao-Xuan Ma, Jun-Peng Jiang, Han-Jia Ye
Machine Learning · Cross-listed from cs.CL
Operadic consistency: a label-free signal for compositional reasoning failures in LLMs
Nathaniel Bottman, Yinhong Liu and Kyle Richardson
Detecting LLM
reasoning failures at inference time without ground-truth labels has motivated a wide range of confidence baselines, including self-consistency, semantic
entropy, and P(True), built on within-question sampling and self-evaluation. Operad theory, the formalism for systems built by iterated substitution, suggests a complementary diagnostic: a model's direct answer to a compositional query should agree with the answer it produces by composing a stated decomposition of the same query. We instantiate this idea as operadic consistency (OC), a per-question signal. Across twelve instruction-tuned LLMs (4B to 671B parameters, open-weights and closed-source) on four multi-hop QA
datasets, OC is strongly correlated with accuracy on every
dataset (Pearson $r \in [0.86, 0.94]$, all $p \leq 0.0004$), and is the only signal we evaluate with $r \geq 0.85$ uniformly across all four
datasets. Chain-of-thought self-consistency (CoT-SC; Wang et al., 2023) matches OC on HotpotQA and DROP ($r = 0.93, 0.87$) but drops to $r \approx 0.45$ on MuSiQue and StrategyQA. At the per-question level, OC contributes information beyond CoT-SC and semantic
entropy on every
dataset (cluster-robust $p \leq 10^{-16}$ for the OC coefficient), and the conclusion is robust to additionally controlling for constructed decomposition-aware baselines ($p \leq 10^{-13}$). The same signal yields selective-prediction improvements (accuracy at fixed coverage) over a tuned CoT-SC baseline at the equal-cost $K = 3$ budget (AUARC lifts of +0.086 to +0.096 and AUROC lifts of +0.092 to +0.164; 95% CIs exclude zero on every
cell). On five frontier thinking models, where the decomposition is extracted from the model's own chain of thought, the same equal-cost comparison gives positive selective-prediction point-estimate lift on all 16 (
dataset, budget, metric)
cells tested, with 95% CIs excluding zero on 12 of the 16.
Read the full abstract ▾
Machine Learning · Cross-listed from cs.CL
SkMTEB: Slovak Massive Text Embedding Benchmark and Model Adaptation
Marek Šuppa, Andrej Ridzik, Daniel Hládek et al. (+2 more)
We introduce SkMTEB, the first comprehensive MTEB-style text
embedding benchmark for Slovak, a low-resource West Slavic
language, comprising 31
datasets across 7 task types – nearly 4$\times$ the depth of existing
multilingual benchmark coverage for Slovak. Our evaluation of 31
embedding models reveals that large instruction-tuned
multilingual models achieve the strongest performance, while existing Slovak-specific models trained for NLU tasks transfer poorly to
embedding tasks. To address the need for efficient, locally-deployable Slovak
embeddings, we develop e5-sk-small (45M parameters) and e5-sk-large (365M) by applying vocabulary trimming and fine-tuning to
Multilingual E5 models. Despite size reductions of up to 62%, our open-source models achieve competitive performance with proprietary APIs while remaining locally deployable for semantic search and
retrieval-augmented generation (RAG). We release the
benchmark, models,
datasets, and code openly, hoping our approach offers a replicable path for other under-resourced
languages.
Read the full abstract ▾
All authors Marek Šuppa, Andrej Ridzik, Daniel Hládek, Natália Kňažeková, Viktória Ondrejová
Machine Learning
The Stable Recovery Manifold: Geometric Principles Governing Recoverability in Continual Learning
Ayushman Trivedi and Bhavika Melwani
Catastrophic forgetting is often viewed as the destruction of previously learned knowledge during sequential learning. Building on the Accessibility Collapse framework, we investigate the
geometric structure of recoverability in continual learning. Using Split CIFAR-100 and a sequentially trained ResNet-18, we analyze recoverability, representational drift, and recovery complexity across ten tasks. We introduce Recovery Subspace Dimensionality (k_t), a measure of the minimum number of singular directions required to preserve 90 percent of full probe performance. Contrary to our Recoverability
Diffusion hypothesis, recovery dimensionality remains stable throughout training (mean k_t = 8.0) despite substantial representational drift. Principal-angle drift strongly predicts recoverability (r = -0.862), and a simple
geometric model explains 82.2 percent of recoverability variance. These findings support the Stable Recovery
Manifold hypothesis, suggesting that forgotten knowledge remains compactly decodable despite representational reorganization. The results indicate that catastrophic forgetting is primarily an accessibility and
manifold-
alignment problem rather than information destruction.
Read the full abstract ▾
Machine Learning · Cross-listed from eess.SY
Aerial Wildfire Suppression Planning with a Hybrid CNN-Cellular Automata Fire Model
Ion Matei, Maksym Zhenirovskyy, Takuya Kurihana et al. (+2 more)
Aerial wildfire suppression requires decisions about when, where, and how to deploy limited aircraft. We present an intervention-design framework built on a frozen hybrid convolutional neural network and cellular automaton (CNN-CA) simulator trained jointly on six historical wildfires. First, we jointly optimize binary drop execution and continuous location and orientation, with aircraft-specific footprints, wind drift, and availability, turnaround, and grounded-day constraints. Second, we model water as an immediate transfer of burning probability to the unburned state and retardant as a persistent reduction in the fuel contribution to spread. Third, we remove drops by a rollout-verified backward elimination while limiting degradation in selected fire-performance metrics. Fourth, we evaluate fixed schedules under daily state sampling and a separate spatially correlated probability-field sensitivity test. Fifth, we compare the optimizer with random, tactical, greedy, and derivative-free planners under shared fleet, drop-budget, and simulator-evaluation allowances, and measure computational scaling. A 2020 Bear Fire case study considers two objectives: total fire-affected area and protected-region exposure. The two-stage total-area schedule reduces deterministic terminal extent by 89.5% relative to the simulator baseline with 1,111 drops. The nominal schedule is sensitive to small pose and effectiveness perturbations. The tactical heuristic is more economical at small evaluation allowances; the gradient planner achieves better objectives with more computation.
Read the full abstract ▾
All authors Ion Matei, Maksym Zhenirovskyy, Takuya Kurihana, Rohit Vuppala, Hon Yung Wong
Machine Learning · Cross-listed from stat.ME
Valid Inference with Synthetic Data via Task Exchangeability
Lezhi Tan and Tijana Zrnic
There is a proliferation of work arguing for the use of synthetic data in scientific research. For example, social scientists are arguing for the use of LLM-generated "silicon samples" in pilot studies; AI evaluations increasingly rely on "LLM-as-a-judge" outputs; and proteomics research is accelerated by generative models that produce synthetic
protein structures. These developments raise an intriguing possibility: synthetic data may help researchers ask more questions, run more studies, and accelerate discovery. But they also raise a fundamental concern: synthetic data can be biased, noisy, and misspecified. In this work, we propose statistical principles for using synthetic data in scientific research with provable validity guarantees. The key insight is a new technical condition that we call task exchangeability. Informally, this is a requirement that the researcher can identify historical tasks, for which real data is available, such that their current task of interest is exchangeable with the historical tasks in an appropriate mathematical sense. We develop methods for valid inference under task exchangeability, together with extensions that provide guarantees even beyond exchangeability. We demonstrate the framework on public opinion surveys with silicon samples and AI evaluation with autoraters.
Read the full abstract ▾
Machine Learning · Cross-listed from cs.SD
Generative Modeling of Bach-Style Symbolic Music: A Comparative Study of Autoregressive, Latent-Variable, and Adversarial Approaches
Dezhi Yu, Kyuil Lee and Yongkang Huang
We study generative modeling of Bach-style symbolic piano music using a shared MIDI corpus and three model families: autoregressive LSTMs with
attention, latent-variable models including recurrent VAEs and vector-quantized VAEs, and generative
adversarial networks. We compare their ability to model polyphonic note sequences, learn useful latent representations, and generate stylistically coherent compositions. Our experiments show that the autoregressive LSTM with
attention produces the most musically coherent samples, while vector quantization helps mitigate posterior collapse and yields more structured outputs than conventional recurrent VAEs. The
adversarial approach captures local pitch patterns but remains difficult to train and generalizes less reliably to Bach's style. These results highlight the relative strengths and failure modes of autoregressive, latent-variable, and
adversarial approaches for symbolic music generation.
Read the full abstract ▾
Machine Learning · Cross-listed from cs.AI
Shielded Analysis: Certification and Characterization of Defensibility in Systems under Adversarial Interaction
Achraf Hsain and Sultan Almuhammadi
Formal safety analysis determines whether a system admits a safe defense; adaptive evaluation characterizes the operating quality sustained under
adversarial interaction. Both answers matter because systems with the same safety verdict can impose very different operational burdens. We introduce shielded analysis, a design-time framework that derives these answers from one encoded system while keeping the safety requirement and admissible threat model independently variable. It returns a defensibility certificate and a four-axis defensibility fingerprint spanning structural margin, shield latitude, and adaptive operating quality. Each axis is informative in its own right; their relationships show whether formal and operational assessments agree, diverge, or respond differently to system changes. We instantiate the framework for network defense on a reference segment and four controlled perturbations spanning
topology, safety requirements, and adversary capabilities. Every configuration is certified defensible, yet two
topology variants with nearly identical structural profiles sustain mean clean-host fractions of 22.7% and 80.7% under adaptive pressure. Shielded analysis turns a safety-game solution into a comparative instrument: it determines whether a defense exists, characterizes what that defense requires, and identifies which system changes strengthen it.
Read the full abstract ▾
Machine Learning · Cross-listed from stat.ML
Majority-of-Three is Optimal
Divit Rawal and Nikita Zhivotovskiy
We give a short proof that the majority vote of three independent consistent classifiers is an optimal learner in the realizable PAC setting. This proves optimality for the simplest voting scheme, while simplifying both the algorithmic structure and the probabilistic analysis of previous voting learners, including the
algorithm of S. Hanneke and the analysis of bagging by K. Green Larsen.
Read the full abstract ▾
Machine Learning · Cross-listed from cs.AI
AgentBeats: Agentifying Agent Assessment for Openness, Standardization, and Reproducibility
Xiaoyuan Liu, Jianhong Tu, Yuqi Chen et al. (+26 more)
Agent systems are advancing quickly across domains, but their evaluation remains fragmented. Most
benchmarks rely on fixed, LLM-centric harnesses that require heavy integration, create test-production mismatch, and limit fair comparison across diverse
agent designs. The root problem is the lack of an open,
agent-agnostic assessment interface. We advocate Agentified
Agent Assessment (AAA), where evaluation is performed by judge
agents and all participants interact through standardized protocols: A2A for task management and MCP for tool access. Conventional benchmarking defines two separate interfaces, one for the
benchmark and one for the
agent, while AAA only needs one; this yields a generic, unified framework that separates assessment logic from
agent implementation and enables reproducible, interoperable, and multi-
agent evaluation. We further introduce AgentBeats as a concrete realization of AAA: we identify five practical operation modes that make standardized assessment compatible with real-world constraints on openness,
privacy, and reproducibility. To evaluate our design at scale, we conduct two studies: a five-month open competition that drew 298 judge
agents across 12 categories together with 467 subject
agents from independent participants, showing that AAA applies across a heterogeneous range of
benchmarks; and a case study on coding
agents that confirms agentified evaluation preserves fidelity with the public record while surfacing previously missing head-to-head results, yielding research insights about
agent design. Combining a community-scale field study and a controlled coding case study, we verify that AAA delivers coverage, practicality, and fidelity across heterogeneous scenarios at scale. Together, AAA and AgentBeats offer a clear path toward open, standardized, and reproducible
agent assessment.
Read the full abstract ▾
All authors Xiaoyuan Liu, Jianhong Tu, Yuqi Chen, Siyuan Xie, Sihan Ren, Tianneng Shi, Gal Gantar, Evan Sandoval, Donghyun Lee, Daniel Miao, Peter J. Gilbert, Nick Hynes, Mauro Staver, Warren He, David Marn, Andrew Low, Xi Zhang, Elron Bandel, Michal Shmueli-Scheuer, Siva Reddy, Alexandre Drouin, Alexandre Lacoste, Ramayya Krishnan, Elham Tabassi, Yu Su, Victor Barres, Chenguang Wang, Wenbo Guo, Dawn Song
Machine Learning · Cross-listed from math.OC
Distribution-Agnostic Robust Trajectory Optimization via Chance-Constrained Reinforcement Learning
Yashdeep Chaudhary, Roberto Armellin, Harry Holt et al. (+1 more)
This paper presents a distribution-agnostic robust trajectory-optimization framework based on chance-constrained reinforcement learning. The uncertainty is represented here through initial conditions and process noise, with the only requirement being that it can be sampled. A deterministic nominal trajectory is first computed offline, and reinforcement learning is then used only to robustify that baseline through a structured affine closed-loop correction law comprising a feedforward control adjustment and time-varying feedback gains. Probabilistic feasibility is enforced empirically through rollout-based upper-tail
quantiles, while terminal dispersion is regulated through covariance-feasibility penalties. The framework is assessed on two materially different trajectory design problems. The flagship case study is a three-dimensional multi-impulse Earth-Mars transfer, where the learned policy is benchmarked against a recent robust trajectory-optimization reference under Gaussian uncertainty and then evaluated under bounded uniform uncertainty and under process disturbances not seen during training. The second case study is a
stochastic atmospheric pinpoint rocket landing problem, used to assess portability to a short-horizon continuous-thrust setting with drag, mass depletion, and glide-slope constraints. The results show that the proposed framework can remain competitive in upper-tail fuel cost while preserving probabilistic feasibility, and that the same robustification scaffold can be carried across heterogeneous spacecraft trajectory planning problems without redesign of its core
stochastic-control structure.
Read the full abstract ▾
All authors Yashdeep Chaudhary, Roberto Armellin, Harry Holt, Marco Sagliano
Machine Learning · Cross-listed from cs.AI
Multi-Agent Reinforcement Learning from Delayed Marketplace Feedback for Objective-Weight Adaptation in Three-Sided Dispatch
Haochen Wu, Yi Hou and Shiguang Xie
Dispatch in three-sided marketplaces provides a natural setting for reinforcement learning from world feedback: decisions are evaluated by delayed operational outcomes such as delivery speed, courier utilization, and merchant congestion. We present a deployed reinforcement learning system at DoorDash that adapts dispatch objective weights in a large-scale food-delivery marketplace using delayed signals. Rather than replacing the combinatorial assignment optimizer, a store-level policy learned from logged marketplace data selects a discrete multiplier that shifts the dispatch optimizer's tradeoff between delivery quality and batching efficiency. This interface enables offline policy learning under noisy, delayed, and coupled feedback while preserving production feasibility constraints and operational safeguards. We train a shared value function using centralized offline data and decentralized store-level execution, with Double Q-learning targets and a conservative regularizer to reduce out-of-distribution value overestimation. In a production switchback experiment, the offline-trained policy increases batching and reduces courier-side time costs without degrading customer-facing delivery quality. Results illustrate how world feedback from a live economic and logistics system can be used to safely adapt decision policies online.
Read the full abstract ▾
Machine Learning
Beyond the Commitment Boundary: Probing Epiphenomenal Chain-of-Thought in Large Reasoning Models
Daniel Scalena, Sara Candussio, Luca Bortolussi et al. (+3 more)
Chain-of-thought (CoT)
reasoning is the dominant paradigm for inference-time scaling in
language models, yet the causal influence of individual steps on the final answer remains poorly understood. In this work, we use answer logits at the end of each
reasoning step to estimate each step's causal importance to the final answer and intermediate guesses, shedding light on the answer formation process of several
reasoning model families. Across diverse tasks, we find that
reasoning typically crosses a commitment boundary, a sharp transition from transient intermediate guesses to a stable, high-confidence answer. This transition often happens in a single step, well before the model's
reasoning block ends, and is followed by epiphenomenal CoT steps that leave the final answer probability unaltered. Using
attention probes, we show that answer-formation stages can be linearly decoded from the activations of intermediate
reasoning steps with high accuracy, showing robust generalization to unseen
reasoning tasks. We leverage this property for early-exiting
reasoning blocks at the commitment boundary location, reducing the length of CoTs up to 55% with negligible impact on model performance.
Read the full abstract ▾
All authors Daniel Scalena, Sara Candussio, Luca Bortolussi, Elisabetta Fersini, Malvina Nissim, Gabriele Sarti
Machine Learning · Cross-listed from cs.AI
Reward Modeling for Multi-Agent Orchestration
King Yeung Tsang, Zihao Zhao, Vishal Venkataramani et al. (+5 more)
Multi-
Agent Systems (MAS) built on Large
Language Models (LLMs) require effective orchestration to coordinate specialized
agents, yet training such orchestrators is hindered by limited supervision and high computational cost. We propose Orchestration Reward Modeling (OrchRM), a self-supervised framework for evaluating orchestration quality without human annotations. OrchRM leverages intermediate artifacts from multi-
agent executions to construct win-lose pairs for Bradley-Terry reward model training. Unlike existing MAS test-time scaling and orchestrator training frameworks that rely on costly sub-
agent rollouts, OrchRM operates directly at the orchestration level, enabling efficient and high-performing reward-guided orchestrator training and MAS test-time scaling. OrchRM improves training efficiency by up to 10x in
token usage while improving MAS test-time scaling performance by up to 8% in accuracy. These gains consistently transfer across multiple domains, including mathematical
reasoning, web-based question answering, and multi-hop
reasoning, demonstrating orchestration-level reward modeling as a scalable direction for robust multi-
agent orchestration. Code will be available at https://github.com/Wang-ML-Lab/OrchRM.
Read the full abstract ▾
All authors King Yeung Tsang, Zihao Zhao, Vishal Venkataramani, Haizhou Shi, Zixuan Ke, Semih Yavuz, Shafiq Joty, Hao Wang
Machine Learning · Cross-listed from cs.AI
Confidence Composition for Multiagent Language Model Systems
Ali Elahi, Michael J. Curry and Barbara Di Eugenio
Multiagent
language model systems, such as collaborative
reasoning and debate, produce multiple correlated candidate answers and confidence signals. However, these signals are usually calibrated only at the individual
agent level, and provide no principled confidence estimate for the system's final answer. We formulate this as a confidence composition problem where combining confidence across
agents and
reasoning stages while preserving both selective utility and probabilistic reliability. We study confidence-aware routing and log-odds pooling protocols that select among candidate answers and output a system-level confidence. Across five
benchmarks, 30 heterogeneous and homogeneous model pairs, and two confidence
estimators, our gated-fusion methods improve AUARC and reduce Brier score over single
agent, standard debate, and selective debate baselines, while retaining competitive weighted F1-score as a correctness metric. We further show that our log-odds fusion is overconfident due to correlated intermediate signals. We propose a shared dependence discount that substantially improves reliability while preserving predictions.
Read the full abstract ▾
Machine Learning
Simplex-Constrained Sparse Bagging: Transitioning from Uniform Priors to Sparse Posteriors in Ensemble Learning
Meher Sai Preetam Madiraju and Meher Bhaskar Madiraju
We present Simplex-Constrained Sparse Bagging (SCSB), a mathematically rigorous framework for post-training compression and probability calibration of bootstrap-based bagging ensembles. Standard bagging ensembles (such as Random Forests, Bagged SVMs, and Bagged Neural Networks) assign uniform voting power to all constituent
estimators. However, this naive uniform prior ignores the varying local competence of base
estimators and contributes to model overconfidence. We formulate ensemble
pruning and calibration as a joint optimization problem over the probability simplex by minimizing the Out-Of-Bag (OOB) loss. To induce sparsity, we address the theoretical "L1-simplex paradox" - the mathematical reality that the L1 norm is constant on the simplex and fails to prune - by introducing a concave quadratic penalty. SCSB is model-agnostic and achieves up to 96% ensemble compression, yielding linear inference speedups and superior probability calibration (lowered Expected Calibration Error) while preserving or enhancing generalization accuracy.
Read the full abstract ▾
Machine Learning · Cross-listed from cs.CL
LabVLA: Grounding Vision-Language-Action Models in Scientific Laboratories
Baochang Ren, Xinjie Liu, Xi Chen et al. (+15 more)
Scientific laboratories increasingly rely on AI systems to reason about experiments, but the physical act of doing science remains largely outside their reach. AI can help read literature, generate hypotheses, and plan protocols, yet the execution of those protocols at the bench still requires a human operator.
Vision-
Language-Action (VLA) models provide one possible interface between written protocols and
robot execution, but existing policies are trained mostly on household and tabletop demonstrations and rarely encounter the instruments, transparent
liquids, or fixed protocol workflows found in scientific laboratories. Closing this gap requires both laboratory-specific supervision and a unified learning framework that can accommodate the diverse
robot embodiments used to execute experimental protocols. We therefore identify data and embodiment as central bottlenecks alongside model design. To address the data side, we build RoboGenesis, a simulation-based workflow and data engine that composes configured laboratory workflows from atomic skills, validates and filters rollouts, and exports structured demonstrations across supported
robot profiles. On the policy side, we present LabVLA, trained with a two-stage recipe: FAST action
token pretraining first makes the Qwen3-VL-4B-Instruct backbone action aware before any continuous control is learned, and flow matching posttraining then attaches a DiT action expert under knowledge insulation. On the LabUtopia
benchmark, LabVLA achieves the highest average success rate among all evaluated baselines under both in-distribution and out-of-distribution settings.
Read the full abstract ▾
All authors Baochang Ren, Xinjie Liu, Xi Chen, Yanshuo Liu, Chenxi Li, Daqi Gao, Zeqin Su, Jintao Xing, Zirui Xue, Rui Li, Xiangyu Zhao, Shuofei Qiao, Minting Pan, Wangmeng Zuo, Lei Bai, Dongzhan Zhou, Ningyu Zhang, Huajun Chen
Machine Learning
Learning with Simulators: No Regret in a Computationally Bounded World
Sasha Voitovych, Abhishek Shetty, Noah Golowich et al. (+1 more)
Understanding the minimal assumptions necessary for generalization is the fundamental question in learning theory. Unfortunately, most results rely heavily on independence (or some proxy thereof) of the data-generating process, while results for strongly dependent data are far more limited. Towards addressing this gap, we introduce the framework of simulatable processes, where the learner has access to a simulator that approximates the distribution generating the data (which may be an arbitrarily complex and dependent process). Surprisingly, given access to such a simulator, we show that we can recover the same learning guarantees as in the classical setting with independent data, namely, error bounds that depend on the VC dimension. Further, we use this framework to study the power of conditional sampling and show strict statistical and computational advantages in this setting. As a highlight of our framework, we exhibit a single
algorithm that simultaneously learns any given VC class under all processes samplable in bounded
polynomial time, with regret controlled by the time-bounded Kolmogorov complexity of the process. This provides a significant conceptual broadening of the classical PAC model.
Read the full abstract ▾
All authors Sasha Voitovych, Abhishek Shetty, Noah Golowich, Alexander Rakhlin
Machine Learning
Existence Precedes Value: Joint Modeling of Observational Existence and Evolving States in Time Series Forecasting
Yifan Hu, Hongzhou Chen, Peiyuan Liu et al. (+3 more)
Real-world time series are often highly incomplete and irregular due to sensor dormancy, transmission delays, and event-driven sampling, making reliable
forecasting fundamentally challenging. Existing methods have evolved from impute-then-forecast pipelines to continuous-time models such as Neural ODEs and continuous-time graph networks. While these approaches improve the modeling of historical irregularity, they still rely on an implicit oracle assumption at inference time: the timestamps of future valid observations are presumed to be known in advance. This assumption limits practical relevance, since in many real systems the more fundamental question is not only what the future value will be, but also whether a valid observation will occur at all. In this paper, we propose Timeflies, a unified framework that reformulates
forecasting as a joint problem of future observability inference and value estimation. To explicitly model the interaction between observation dynamics and state
evolution, Timeflies adopts an observation stream and a value stream, coupled through three dedicated modules for reliability-aware
embedding, observation-guided dependency modeling, and joint prediction. We further construct Shadow, a
benchmark that combines natural missingness from public
datasets with real-world industrial data, and introduce the Observation-Value Joint
Entropy (OVJE) metric to comprehensively evaluate this coupled predictability. Extensive experiments show that Timeflies consistently outperforms existing methods, highlighting the importance of explicitly modeling future observability in time series
forecasting with missing values. Code and
dataset are available in https://github.com/ant-intl/Timeflies.
Read the full abstract ▾
All authors Yifan Hu, Hongzhou Chen, Peiyuan Liu, Yiding Liu, Zewei Dong, Jiang-Ming Yang
Machine Learning
Adjusted Cup-Product Neural Layer
Snigdha Chandan Khilar
Many important observables in physics and geometry are cup products of cochains. The adjusted cup product neural layer has been introduced in this paper. It is a neural primitive that hard wires the cup product with an adjustment term from higher gauge theory. This creates a readout that is gauge invariant by design. Their main theoretical result shows that on a closed cycle the output relies entirely on the adjustment coefficient. Setting this coefficient to zero removes the output completely regardless of other parameters. Thus the adjustment is the only source of gauge invariant signal. They prove this observable is a nonzero quadratic form and is exactly invariant under one and two gauge transformations.
Read the full abstract ▾