Artificial Intelligence · Cross-listed from cs.RO
CoStream: Composing Simple Behaviors for Generalizable Complex Manipulation
Haonan Chen, Yuxiang Ma, Stephen Tian et al. (+7 more)
Long-horizon, contact-rich complex manipulation tasks, such as seating a GPU into a PCIe slot, demand both millimeter high precision and out-of-the-box generalization to new tasks. Existing paradigms struggle to satisfy both: classical pipelines use brittle, task-specific interfaces to achieve high-precision control but require costly pipeline redesigns to adapt to new tasks, whereas monolithic end-to-end policies provide better generalization but lack high precision on complex, out-of-distribution tasks unless retrained with new data. Both paradigms share an implicit assumption: once a manipulation capability is acquired, it must be deployed as a rigid pipeline or monolithic whole, rather than being freely decomposed and recomposed. In this paper, we show that complex manipulation capabilities can emerge naturally from the composition of simple, independent behaviors. Rather than deploying a monolithic policy or a rigid pipeline, we propose CoStream, a framework orchestrating foundation models and diverse sensing modalities into multiple composable core behaviors: a semantic behavior extracting spatial constraints via foundation models; a predictive behavior
forecasting trajectories by tracking keypoints in imagined
videos; and a reactive behavior providing high-frequency tactile and force corrections. On a shared $SE(3)$ interface, these outputs compose by right-multiplication into a single pose command at each control step, executed by a compliant controller. We demonstrate CoStream on 8 real-world tasks spanning everyday manipulation and precision assembly, with the strongest gains in contact-rich assembly and object transfer, and show robust recovery from manual perturbations during execution. Website: https://costream-simple.github.io
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All authors Haonan Chen, Yuxiang Ma, Stephen Tian, Xiaoshen Han, Wenlong Huang, Feiyang Wu, Yunzhu Li, Jiajun Wu, Edward H. Adelson, Yilun Du
Artificial Intelligence
Estimating Uncertainty in Classifier Performance with Applications to Large Language Models and Nested Data
Kylie Anglin
Researchers increasingly use text classification–supervised models or large
language models–to measure constructs from natural
language, providing metrics such as recall and precision as evidence of their validity. Yet, though these metrics are point estimates subject to sampling variation, measures of uncertainty are inconsistently reported alongside them. Further, when they are reported, they are often estimated with methods that are not appropriate when relevant labelled
datasets are small or performance is high. To increase and improve confidence interval reporting in the field, this paper evaluates confidence interval methods for performance metrics under conditions typical of social science text classification: small to moderate sample sizes, infrequent constructs, and texts nested within individuals. Across simulations, default methods such as the Wald interval and the basic percentile bootstrap are the least accurate, with coverage sometimes far below the nominal 95% level. Accuracy is improved with the use of Agresti-Coull, Wilson, Clopper-Pearson, and a novel pseudo-count regularized bootstrap (which is particularly relevant to the calculation of F1). When texts are nested within individuals, we demonstrate that adjustment for both effective N and the appropriate degrees of freedom is necessary for producing accurate analytic intervals. Among bootstrap intervals, the hierarchical bootstrap is more accurate than the cluster bootstrap when individuals produce a moderate number of texts but overly conservative when individuals produce only a few. By providing guidance to the field on appropriate interval estimation, we aim to improve the transparency of machine learning applications, and to encourage greater
attention to the validation sample size at the design stage.
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Artificial Intelligence
Unbiased Canonical Set-Valued Oracles Via Lattice Theory
Jobst Heitzig
An oracle that tells you the probability of some future event can change that very probability because you act on the answer. We argue that this performativity is OK as people consult oracles to be informed, and hence moved, by the answer. We worry about instead that asking for a self-consistent answer, one that still holds once it has been announced, may leave the oracle with several answers to pick from, and whichever rule it uses to pick is a lever it could learn to pull. We propose to take away that choice: The oracle reports a credal set instead of a point estimate, which lifts the oracle's reaction function to an isotone operator on a
lattice. We make the oracle report that operator's least fixed point, which exists because of Knaster and Tarski. As that answer is fixed by a rule laid down in advance, nothing is left to choose by the oracle. We show that solution exists, is self-consistent, is never empty, can be computed by iterating from below, and equals the ordinary point estimate if the question is not performative after all. For simple queries about probabilities, we propose to restrict answers to intervals and show that, under a mild monotonicity assumption, the answer is simply the interval from the no-information baseline to the self-fulfilling
equilibrium one would end up at by iteratively querying a point oracle until the answer is self-consistent. As our proposal is purely order-theoretic, it carries over unchanged to arbitrary random variables and to bounded continuous statistics of their law. Finally we show that a fixed finite family of polytopes suffices to approximate every answer uniformly, which turns the construction into a terminating computation. We close by placing the construction inside the Scientist AI programme, where it offers a choice-free criterion for a step that programme currently hands to audited human judgement.
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Artificial Intelligence · Cross-listed from cs.LG
Beyond Feedforward Networks: Reentry Neural Systems as the Fundamental Basis of Subjecthood and Intrinsic Safety of Next-Generation AGI
A. S. Ushakov and Yu. N. Berdinsk
We propose a complete architectural blueprint for safe artificial general intelligence based on a closed reentry loop (D <-> I cycle). In contrast to feedforward networks, which are directed acyclic graphs (C=0, S=0) incapable of self-reference, the proposed architecture contains a structural cycle (C >= 1) with self-sustaining amplification (rho > 1), mathematically guaranteeing the emergence of a self-model, instrumental self-preservation, and unprogrammed goal-directed behaviour. The
agent's goals are encoded as a non-textual D-vector in the architecture itself, making them immune to reinterpretation and
prompt injection. We present the S-measure – a
polynomial-time [O(N^3)] computable alternative to Tononi's NP-hard Phi – with machine-verified Lean 4 proof that S>0 implies positive integrated information. The work provides full Python/NumPy implementations (Tarjan-based cycle complexity, Delta-S barrier), industrial horizontal scaling via Apache Kafka and Docker Compose, a taxonomy of six epochs of AI
evolution, a zoo of future reentry architectures (RAS,
diffusion attractors, fractal loops), gauge-invariant networks for safe swarms, fault-tolerance and recovery protocols, and eight falsifiable predictions. All formal proofs are machine-verified in Lean 4. This architecture is deployable today and represents a topologically protected, safe-by-design approach to AGI.
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Artificial Intelligence
When Agents Meet Electric Bus Fleet Operations: Pricing Behavior, Trade-offs, and Policy Implications in an Aggregator Framework
Jônatas Augusto Manzolli, Ali Eslami, Luis Miranda-Moreno et al. (+1 more)
Agentic systems are changing how complex operational tasks are coordinated, introducing a new paradigm for connecting heterogeneous data sources and automating processes. Electric bus fleets provide a relevant test case. Their operation requires continuous coordination between service reliability, battery state-of-charge, charger availability, electricity prices, route-energy uncertainty, and vehicle-to-grid (V2G) opportunities. This paper proposes an agentic aggregator framework that streamlines this decision environment by coupling an optimization-based electric bus scheduling model with supervisory
agents for disturbance detection, tariff adaptation, and schedule evaluation. The optimization core enforces physical feasibility across routes, chargers, batteries, and V2G exchanges, while the agentic layer interprets changing operating conditions, triggers real-time re-optimization when needed, and defines how flexibility value is allocated between the aggregator and the public transport operator (PTO). A realistic depot case study evaluates day-ahead and real-time operations under profit-based and operation-based coordination modes, considering service delays, route-energy deviations, electricity price shocks, and combined disturbances. The results show that agentic aggregation can support adaptive fleet-grid coordination by maintaining feasible schedules, activating re-optimization selectively, and improving the use of charging and V2G flexibility. However, they also reveal a critical trade-off: the same agentic capability that reduces operational complexity can extract value from the PTO when configured around profit-oriented pricing. These findings suggest that agentic aggregators can become useful for managing electric bus V2G operations, but their deployment in public-fleet contexts requires transparent coordination modes, auditable tariff-setting, and explicit value-sharing rules.
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All authors Jônatas Augusto Manzolli, Ali Eslami, Luis Miranda-Moreno, Jiangbo Yu
Artificial Intelligence
Geometry-Aware MCTS for Extremal Problems in Combinatorial Geometry
Luoning Zhang, Xu Zhuang, Tianhao Wang et al. (+1 more)
We study certain extremal problems in combinatorial geometry that ask about configurations of points in an $n \times n$ grid that satisfy strict, global
geometric constraints. Classical exact solvers suffer from combinatorial explosion for these types of problems, and standard reinforcement learning and
transformer-based models struggle with the sparse reward "validity cliff" and quadratic
token-consumption limits. To overcome these bottlenecks, we propose a Geometry-Aware Monte Carlo Tree Search (MCTS) framework. Our approach strictly enforces
geometric constraints through incremental updates to the feasible action space. For constraints about collections of collinear points, like those that occur in the classic No-Three-in-Line problem (Max-N3IL), this mechanism reduces the constraint checking complexity from $O(n^3)$ to $O(n^2)$. To improve search efficiency, we exploit
geometric symmetries in two ways: canonical
pruning during node expansion to reduce the branching factor, and symmetric batch transitions to accelerate the discovery of promising configurations. We perform extensive experiments and establish new best-known computational results on five out of six of the problems that we considered. Notably, for Max-N3IL we find configurations of size roughly $1.8 n$ for grids of size $82 \le n \le 119$. For the Smallest Complete Set problem, we find configurations of size roughly $0.95 n$, providing new upper bounds within the tested grids. This work establishes Geometry-Aware MCTS as a highly adaptable framework for discovering novel configurations in combinatorial geometry.
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All authors Luoning Zhang, Xu Zhuang, Tianhao Wang, Nathan Kaplan
Artificial Intelligence · Cross-listed from cs.LG
Deterministic Pareto-Optimal Policy Synthesis for Multi-Objective Reinforcement Learning
Aniruddha Joshi, Niklas Lauffer and Sanjit Seshia
Real-world decision-making often requires balancing multiple conflicting objectives, a challenge that standard Reinforcement Learning (RL) frequently addresses by aggregating rewards into a single scalar signal. While effective for simple tasks, this approach often fails to capture the full spectrum of optimal trade-offs, known as the Pareto frontier. In this paper, we introduce a novel preference-conditioned Bellman operator, motivated from the Chebyshev scalarization, designed to compute deterministic Pareto-optimal policies for Multi-Objective Markov Decision Processes (MOMDPs). We prove that this operator satisfies an enveloping property, where the estimated value functions upper-bound the true Pareto frontier, and demonstrate that it monotonically converges to a coverage set of this frontier. Furthermore, we also show how to extract deterministic policies from these converged Q-estimates. This ensures the
agent can recover a policy for any given preference, capturing the entire Pareto-optimal frontier while guaranteeing each synthesized policy remains approximately Pareto-optimal. Experimental results validate that our
algorithm successfully recovers complex trade-offs, providing a solution for deterministic Pareto-optimal policy synthesis.
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Artificial Intelligence · Cross-listed from physics.ao-ph
Sampling sea state using a diffusion model
Jiarong Wu, Bertrand Chapron and Laure Zanna
Sea state prediction is essential for operational maritime applications and coupled earth system modeling, yet current spectral wave models remain computationally prohibitive for many use cases, including online coupling to
climate simulations and making probabilistic (ensemble-based) predictions. While deep learning has recently demonstrated strong performance in weather
forecasting, existing AI-based wave models are predominantly deterministic and largely limited to bulk variables such as significant wave height, leaving probabilistic sea state estimation largely unexplored. In this work, we propose a
diffusion-based generative model for global sea state estimation that conditions on a relatively long history (5 days) of global wind forcing. This generative model directly samples the complex conditional distribution of sea state without autoregressive time-stepping. Unlike prior approaches, our framework naturally extends beyond bulk variables to estimate partition-related variables and derived quantities, such as Stokes drift and mean square slope. Trained on a 30-year global WAVEWATCH-III hindcast, the model achieves substantial computational acceleration compared with numerical spectral models while delivering skillful predictions and a calibrated ensemble spread for the bulk variables. Our results suggest that
diffusion-based sea state sampling offers a promising path toward probabilistic wave
forecasting and efficient coupling of sea state information into broader earth system models.
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Artificial Intelligence · Cross-listed from cs.LG
SOLAR: AI-Powered Speed-of-Light Performance Analysis
Qijing Huang, Sana Damani, Zhifan Ye et al. (+9 more)
How fast could a deep-learning model run on target hardware, and how far is today's implementation from that limit? These questions are central to software, hardware, and
algorithm optimizations. Speed-of-Light (SOL) analysis answers them by computing a workload's theoretical minimum execution time on a given architecture. Yet deriving SOL bounds remains manual, error-prone, and disconnected from rapid model development. To close this gap, we introduce
SOLAR, a framework that automatically derives validated SOL bounds from PyTorch and JAX source code.
SOLAR leverages both generative and deterministic components in its flow: an LLM frontend translates any source programs into an executable Affine Loop IR, validated by output comparison; a deterministic flow lifts the IR into an einsum graph; and an analytical backend computes unfused, fused, and cache-aware SOL bounds.
SOLAR provides comprehensive operator and
language coverage, produces validated bounds with zero observed SOL violations, and offers multi-fidelity analysis that tightens bounds and surfaces optimization insights. We evaluate
SOLAR across KernelBench, JAX/Flax models, and robotics workloads. These experiments demonstrate four use cases: headroom analysis at multiple fidelity levels, identifying optimization opportunities, cross-platform exploration, and inverse-roofline hardware provisioning.
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All authors Qijing Huang, Sana Damani, Zhifan Ye, Athinagoras Skiadopoulos, Siva Kumar Sastry Hari, Jason Clemons, Sahil Modi, Jingquan Wang, Aditya Kane, Edward C Lin, Humphrey Shi, Christos Kozyrakis
Artificial Intelligence · Cross-listed from cs.CL
Charting the Growth of Social-Physical HRI (spHRI): A Systematic Review Pipeline Augmented by Small Language Models
Mayumi Mohan, Ju-Hung Chen and Alexis E. Block
Social-physical human-
robot interaction (spHRI) has grown rapidly across robotics, human-computer interaction, human-
robot interaction, and haptics. Yet, fragmented terminology and inconsistent methodologies make systematic synthesis difficult. To support scalable review practices, we evaluated the extent to which small
language models (SLMs; < 1.5B parameters) can assist with title and abstract screening for a large spHRI systematic review. While no SLMs matched human reviewers' performance, the models operated locally and screened papers orders of magnitude faster. The combined SLM ensemble identified 39 papers reviewers missed, representing 10.29% of the final relevant
dataset. These results demonstrate that SLMs can augment, rather than replace, expert reviewers and make large-scale literature reviews accessible and sustainable.
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Artificial Intelligence · Cross-listed from cs.CR
Hybrid privacy-aware semantic search: SVD-truncated document geometry and CKKS-encrypted query reranking under a restricted threat model
Sergey Kurilenko
Semantic search creates an asymmetric disclosure problem: query
embeddings may reveal user intent, while returning exact provider vectors distributes reusable representations. We evaluate a deliberately restricted hybrid design. A public corpus-fitted SVD basis and PQ index support client-side candidate selection; candidate IDs are disclosed, while the projected query is encrypted with CKKS. A block-SIMD kernel scores 100 plaintext provider vectors and returns one ciphertext. At 672 dimensions, median server time falls from 1689.1 to 224.5 ms and response size by a factor of 99.5; provider-only saturation reaches 25.99 requests/s with 16 workers. In a frozen post-exploratory revision-analysis subset, disjoint from validation and containing 3,235 canonical BEIR queries, five of six collections satisfy a +/-0.002 nDCG@10 equivalence rule between actual CKKS and plaintext reranking of the same shortlist, while ArguAna is inconclusive. Projection controls favor SVD over random and coordinate truncation. Leakage audits show that disclosed candidate sets are highly linkable and reproduce part of the exact neighbourhood, while public PQ reveals approximate corpus geometry. The design therefore conditionally hides numerical query slots, but does not provide semantic-query, document, unlinkability,
circuit, or access-pattern
privacy.
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Artificial Intelligence
Narration-of-Thought: Inference-Time Scaffolding for Defeasible Ethical Reasoning in Large Language Models
Patrick Cooper and Alvaro Velasquez
Standard chain-of-thought on moral dilemmas exhibits two failure modes: stakeholder collapse (the trace names at most one party with a stake in the outcome) and uncertainty suppression (no explicit unknowns or hedges before committing to an action). We introduce narration-of-thought (NoT), a system
prompt that structures chain-of-thought into five sections: protagonist, stakeholders, two-step consequences, uncertainty, then commitment. NoT adds no training, parameters, or fine-tuning. On 100 DailyDilemmas scenarios across four generators from three vendors, NoT cuts stakeholder collapse from up to 31% to under 1% and uncertainty suppression from up to 72% to 1-24% on every model. A matched-budget verbose-CoT control rules out
token spend as the active ingredient; NoT retains Cliff's delta advantages of +0.79 to +0.90 on stakeholder count and +0.65 to +0.93 on uncertainty score for three of four generators, and a section ablation attributes each shift to its specific sub-instruction. Textual-gradient descent initialised at NoT improves the scaffold further; a cross-family training judge (different vendor from the generator) dominates an in-family one on every measured axis. Extended to a five-round multi-stakeholder debate protocol, the scaffold converts a 6% standoff into 95% full consensus on a calibration set and 100% combined convergence on a DailyDilemmas replication. The resulting traces externalise the stakeholders, consequences, and uncertainty grounding each commitment, providing an auditable substrate for dependable agentic deployment.
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Artificial Intelligence
Accelerating Returns and the Qualitative Engine for Science
Guojun Liao
Ray Kurzweil described a thesis of accelerating returns, which is the most influential narratives in discussions of technological progress. Its central claim is that advances in multiple technological fields, especially compute, artificial intelligence,
brain science, and biotechnology, interact in such a way that progress becomes self-amplifying and approximately exponential. This paper gives a simple mathematical interpretation of that claim and then argues that, even if such acceleration is real, it does not by itself resolve the central problem of scientific discovery. The reason is that accelerating returns apply most naturally to executional and infrastructural capability, whereas genuine discovery often depends on a different capacity: qualitative
reasoning about when a current framework is structurally inadequate and what conceptual move is needed next. Recent ARC-AGI-3 results sharpen this distinction: humans solve the
benchmark at ceiling, whereas frontier AI systems remain below 1%, indicating that the gap between current AI and human flexible
reasoning is still very large. At the same time, Demis Hassabis has emphasized that humans must retain their sense of meaning and what they choose to focus their lives on, a reminder that the future of AI is not only a technical forecast but also a question of what forms of human understanding are worth preserving and transmitting. This paper positions the Qualitative Engine for Science (QES) [3] as a response to that missing capacity. In this view, the Kurzweil theory helps explain why quantitative capability may accelerate, while QES addresses the central problem in scientific discovery that acceleration alone does not solve. Its value does not depend on when AGI arrives, but on the fact that the processes of scientific discovery themselves constitute a form of human wisdom worth preserving, organizing, and making accessible.
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Artificial Intelligence
Instruction Bleed: Cross-Module Interference in Prompt-Composed Agentic Systems
Ching-Yu Lin and Yifan Liu
Practitioners of
prompt-composed agentic systems report a recurring failure mode: editing one
prompt module silently shifts the behavior of others despite no shared variable or executable dependency. We formalize this as compositional behavioral leakage (CBL): interference between modules sharing a context window. CBL is enabled by architectural non-isolation:
transformer self-
attention provides no formal boundary between concatenated modules. We probe CBL on a deployed job-evaluation
agent (Claude Sonnet 4.6, 144 trials) through a reusable three-channel protocol that perturbs non-focal modules along volume, content, and form. Only the content channel produces a detectable paired effect (Cohen's d = 0.63, bootstrap 95% CI excluding zero); no recommendation flipped – a sub-threshold regime invisible to standard QA but compounding across the thousands of decisions a deployed
agent makes. CBL is orthogonal to known
agent-failure axes (
adversarial injection, cognitive degradation, multi-
agent fault propagation,
privacy leakage). We contribute an operational definition, a reusable protocol, a falsifiable prediction set, and a system-class characterization, establishing cross-module interference measurement as a requirement for
prompt-composed
agent evaluation.
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Artificial Intelligence
OpenFinGym: A Verifiable Multi-Task Gym Environment for Evaluating Quant Agents
Kaicheng Zhang, Wen Ge, Lei Jiang et al. (+5 more)
Although large
language model
agents are increasingly applied to quantitative-finance workflows, their evaluation remains fragmented across isolated tasks, while the financial relevance of
benchmark tasks is often overlooked. Yet financial workflows are inherently multi-stage, spanning interdependent tasks such as
forecasting, strategy construction, risk management, and trading. Existing platforms typically focus on a single task, and can therefore overstate
agent competence and fail to reveal weaknesses in generalization, real-
market interaction, and financially meaningful decision-making. We introduce OpenFinGym, a unified gym environment for quantitative-finance
agent development that covers
forecasting,
market generation, real-time trading, and fraud detection under a single execution and verification interface. OpenFinGym additionally provides an automated task-construction pipeline that turns quantitative finance publications into executable task packages; a containerised runtime with a host-side verifier service that supports scalable
agent rollouts and prevents runtime train-test leakage; a paper trading engine with a low-latency data-stream design; deferred-resolution support for long-horizon and event-
market forecasts; and integration for SFT and RL post-training.
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All authors Kaicheng Zhang, Wen Ge, Lei Jiang, Weixin Yang, Jordan Langham-Lopez, Jialin Yu, Lukasz Szpruch, Hao Ni
Artificial Intelligence
What We are Missing in Multimodal LLM Evaluation?
Po-han Li, Shenghui Chen, Sandeep Chinchali et al. (+1 more)
Multimodal large
language models (MLLMs) can process diverse inputs, e.g., text, images, audio, and
video, and generate textual responses. While their capabilities have advanced rapidly, evaluation of such models has not kept pace. Most existing evaluation
benchmarks are limited to isolated tasks and reveal little about whether a model integrates information across modalities. We examine current means for evaluating MLLMs and review the existing
benchmark taxonomy to identify gaps, including temporal-spatial coherence, physical world understanding,
multimodal consistency, and selective
attention. Addressing these gaps is essential for measuring real progress in
multimodal intelligence and exposing capability boundaries.
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All authors Po-han Li, Shenghui Chen, Sandeep Chinchali, Ufuk Topcu
Artificial Intelligence
How Do Tool-Augmented LLM Agents Perform on Real-World Energy Analytics Tasks?
David Akinpelu, Akintonde Abbas, Rereloluwa Alimi et al. (+1 more)
Agentic
benchmarks have emerged across general-purpose and domain-specific settings, including finance, coding, law, and drug discovery, yet energy-domain evaluations remain largely limited to static knowledge recall. This is a critical gap for a sector that requires live data
retrieval, specialized regulatory and
market knowledge, and multi-step quantitative
reasoning under real-world constraints. We present an empirical study of tool-augmented LLM
agents on real-world energy
market analytics tasks. Our evaluation environment includes 243 expert-curated problems across three categories: (1)
Market Data
Retrieval and Analysis, (2) Knowledge
Retrieval and Interpretation, and (3) Advanced Quantitative Modeling and Decision Analytics. Tasks include price and demand analysis, tariff impact modeling, asset revenue and returns estimation, hedging strategy analysis, and optimization modeling, with problems spanning multiple difficulty levels.
Agents are equipped with a configurable suite of domain tools, including live electricity
market APIs for major U.S. ISOs, regulatory docket search, utility tariff databases, asset optimization models, and
retrieval-augmented generation over energy
market documents. We assess
agent responses using a multi-dimensional evaluation protocol that scores approach correctness, answer accuracy, attribute
alignment, and source validity, with category-aware routing to match scoring criteria to question type. We evaluate both closed-source and open-source LLMs, providing a comparative analysis of how model capability and domain tooling interact in a high-stakes professional domain. Key artifacts are publicly released to support reproducibility and future research.
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All authors David Akinpelu, Akintonde Abbas, Rereloluwa Alimi, Ayodeji Lana
Artificial Intelligence · Cross-listed from cs.LG
EVOM: Agentic Meta-Evolution of Actor-Critic Architectures for Reinforcement Learning
Boyun Zhang, Chao Wang and Kai Wu
In actor-critic reinforcement learning, network architectures are typically manually designed. Automating this design is challenging because each candidate must be trained before evaluation, and the design space is open-ended. To address these challenges, we introduce EVOM, an agentic meta-
evolution framework for discovering high-performance actor-critic architectures. We frame architecture search as a bi-level optimization: an inner loop trains weights via the low-fidelity proximal policy optimization (PPO), while an outer loop drives meta-
evolution by iteratively refining architecture programs. Crucially, this outer loop is powered by an LLM-based design
agent that operates purely as an architecture designer, completely decoupled from policy execution and environment control. Experiments reveal that EVOM outperforms the manually designed baseline, an LLM-guided random search, and the state-of-the-art LLM-guided programmatic policy search method MLES, delivering superior performance on Ant-v4 and HalfCheetah-v4. Ablation studies validate that both the meta-
evolution loop and the LLM Design
Agent are indispensable for final performance.
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Artificial Intelligence · Cross-listed from eess.SP
Parametric Generalized Adaptive Moment Features (PG-AMF) for Bearing Fault Diagnosis and Machine Health Monitoring
Rajeev Kumar
Accurate fault diagnosis of rolling element bearings in rotating machinery is considered essential for ensuring industrial safety and enabling predictive maintenance. Conventional statistical feature-based methods rely on predefined descriptors, whose diagnostic sensitivity is constrained by fixed configurations and limited adaptability across varying fault conditions. Although deep learning approaches offer strong representational capacity, their effectiveness is often restricted by high data requirements and reduced interpretability. In this work, a parametric adaptive feature extraction framework is proposed, in which feature characteristics are learned directly from data rather than being manually specified. Multiple complementary representations are extracted from vibration signals, including absolute features capturing signal energy distribution, signed moment features reflecting waveform asymmetry, and AC-coupled moment features emphasizing dynamic fluctuations, while interactions between multiple sensor channels are modeled through a structured fusion mechanism to enhance fault representation. The proposed approach is evaluated on a
benchmark gearbox bearing
dataset comprising five health conditions, including normal operation and multiple fault types. Improved classification performance is observed compared to conventional methods, with consistent results under cross-validation, indicating strong generalization capability. Additionally, enhanced feature separability is demonstrated through clearer clustering patterns in low-dimensional projections. The learned representations effectively capture a wide range of signal characteristics, supporting both improved diagnostic performance and practical applicability in industrial monitoring systems.
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Artificial Intelligence
The Verification Horizon: No Silver Bullet for Coding Agent Rewards
Binghai Wang, Chenlong Zhang, Dayiheng Liu et al. (+10 more)
A classical intuition holds that verifying a solution is easier than producing one. For today's coding
agents, this intuition is being inverted: as foundation models develop stronger
reasoning capabilities and engineering harnesses grow more sophisticated, generating complex candidate solutions is no longer difficult – reliably verifying them has become the harder problem. Every verifier we can build is only a proxy for human intent, never the intent itself. This makes verification subject to a twofold difficulty: first, intent is underspecified by nature, making it inherently hard to faithfully check whether it has been fulfilled; second, during model training, optimization widens the gap between proxy and intent – manifesting as reward hacking or signal saturation. To address this, we characterize the quality of verification signals along three dimensions – scalability, faithfulness, and robustness – and argue that achieving all three simultaneously is the central challenge. We further study four reward constructions: a test verifier for general coding tasks, a rubric verifier for frontend tasks, the user as verifier for real-world
agent tasks, and an automated
agent verifier for long-horizon tasks. Across different task types and policy capability levels, we conduct in-depth analysis and experiments on the core challenges of reward design and how to more effectively leverage reward signals. Experiments show that targeted verification design can effectively suppress reward hacking, improve task completion quality, and achieve significant gains across multiple internal and public
benchmarks. These experiences collectively point to a core observation: no fixed reward function can remain effective as policy capability continues to grow; and verification must co-evolve with the generator.
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All authors Binghai Wang, Chenlong Zhang, Dayiheng Liu, Jiajun Zhang, Jiawei Chen, Mingze Li, Mouxiang Chen, Rongyao Fang, Siyuan Zhang, Xuwu Wang, Yuheng Jing, Zeyao Ma, Zeyu Cui
Artificial Intelligence
COrigami: An AI Pipeline for Co-Designing Flat-Foldable Visually Recognisable Origami
Tom Zahavy, Shaobo Hou, Thomas Tumiel et al. (+16 more)
While generative AI has achieved remarkable success in solving problems with verifiable solutions, generating physical art that satisfies both strict
geometric constraints and subjective visual aesthetics remains a challenge. This paper presents an approach to tackle these difficulties in the domain of computational origami, a mathematically rigid environment that grounds artistic design within the equations of flat foldability. We present COrigami, an end-to-end AI-driven pipeline that assists the design cycle by generating crease patterns from natural
language. Our pipeline involves generating a semantic stick figure, computing a base packing, solving for a flat-foldable crease pattern, shaping the flat-folded crease pattern, and refining the generated model using reinforcement learning driven by an autonomous aesthetic evaluation loop. Our system acts as a highly effective collaborative assistant, generating structural starting points that human artists can further expand and shape. By integrating algorithmic optimisation with autonomous aesthetic critique, this work demonstrates how AI systems can satisfy multi-objective physical constraints to enable reliable, mathematically grounded co-creativity.
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All authors Tom Zahavy, Shaobo Hou, Thomas Tumiel, James Doran, Francesco Faccio, Xidong Feng, Alex Havrilla, Igor Khytryi, Chenglei Li, Lisa Schut, Vivek Veeriah, Arijan Abrashi, Michał Kosmulski, Robert J. Lang, Nick Robinson, Brandon Wong, Marcus Chiam, Gloria Fang, Satinder Singh
Artificial Intelligence
Governing Actions, Not Agents: Institutional Attestation as a Governance Model for Autonomous AI Systems
Jakob Salfeld-Nebgen
Autonomous AI
agents may begin to perform consequential, irreversible actions such as clinical prescribing and production software deployment. This paper observes that human institutions have governed powerful autonomous actors not by monitoring their
reasoning but by requiring independently attested evidence at the point of consequential action. We formalise this institutional pattern as a computational governance model for AI
agent systems. Under the proposed model, an
agent retains full autonomy over planning and
reasoning but holds no execution authority over designated high-risk actions. Execution is conditional on preconditions that are each independently attested by a separate authoritative source, cryptographically bound to a declared intent, and evaluated by a deterministic policy. Decisions are recorded in a tamper-evident log amenable to independent re-verification. We present a proof-of-concept implementation and illustrate the model with examples from software deployment and clinical prescribing.
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Artificial Intelligence · Cross-listed from cs.LG
The Red Queen Gödel Machine: Co-Evolving Agents and Their Evaluators
Alex Iacob, Andrej Jovanović, William F. Shen et al. (+12 more)
Self-improving
agents are state-of-the-art on agentic coding
benchmarks, yet their search methods assume a stationary evaluation criterion. This ignores a central feature of
evolution: species adapt as their environments change with them. We introduce the Red Queen Gödel Machine (RQGM), an evolutionary framework for recursive self-improvement under non-stationary utilities. This allows learned evaluators to improve alongside the
agents they guide. On DeepSWE, the RQGM improves over its fixed-evaluator baseline by adding a complementary
agent-as-a-judge code-review signal: a co-evolved reviewer grades coder patches to guide search. At low
reasoning effort, the RQGM coder passes 82.1% of held-out tasks against the baseline's 75.0%, and nearly matches the GPT-6 Astra model at high effort. In scientific paper writing and reviewing, and Olympiad-level proof writing and grading, co-evolved evaluators provide an evaluation criterion. Anchored to human IMO grades, a co-evolved grader writes its own milestone rubric and exceeds static baselines at a 3x lower search cost, driving the prover to the best mean score. Since the RQGM can modify the search objective across epochs, it can regularize the search. For example, the RQGM reduces self-preference bias via an additional
adversarial objective to discover reviewers equally stringent on AI and human work. Guided by these calibrated reviewers, co-evolved writers reach 1.78x-1.86x higher acceptance rates than the baseline under an
agent-as-a-judge panel. The RQGM enables self-improving systems where
agents and evaluators recursively bootstrap each other beyond static evaluation.
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All authors Alex Iacob, Andrej Jovanović, William F. Shen, Daniel Burkhardt, Meghdad Kurmanji, Nurbek Tastan, Lorenzo Sani, Niccolò Alberto Elia Venanzi, Jiayi Nie, Ambroise Odonnat, Zeyu Cao, Bill Marino, Xinchi Qiu, Rika Antonova, Nicholas D. Lane
Artificial Intelligence · Cross-listed from cs.LG
SSM Adapters via Hankel Reduced-order Modeling: Injection Site Determines Task Suitability in Long-Context Fine-Tuning
Omanshu Thapliyal
While parameter-efficient fine-tuning (PEFT) typically targets
attention projectors, its efficacy for tasks requiring sequential state accumulation remains under-explored. We examine if PEFT for such tasks can benefit from state space model (SSMs) adapters, and if MLP blocks are better injection sites. We introduce Hankel Reduced order Model (HRM) adapter, an SSM-based residual module initialized via Balanced Truncation of empirical Hankel Grammians. By leveraging the time-invariance of the system
matrix $\bar{A}$, HRM enables an exact FFT-based parallel scan, achieving computational parity with LoRA across all context lengths. In iso-parametric evaluations on Mistral-7B (8.4M trainable parameters), HRM outperforms LoRA variants on LongBench tasks, including QuALITY (+34.8% relative accuracy) and QMSum (+71.6% relative ROUGE-1). HRM further demonstrates consistent superiority across 18 configurations of synthetic state-tracking (DFA, Parity) and character-level
language modeling (enwik8). Gate analysis reveals that HRM adapters effectively learn to modulate recurrence, providing a robust architectural alternative to low-rank adaptation for long-context sequence modeling.
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Artificial Intelligence · Cross-listed from cs.CR
TEMPO-Diffusion: Temporally Exposed Malicious Poisoning of Diffusion Models
William Aiken, Paula Branco, Guy-Vincent Jourdan et al. (+1 more)
Noise-based
backdoor attacks on
diffusion models typically rely on input-time trigger injection, untargeted activation, and out-of-distribution target generation. Such assumptions reduce both the stealthiness and the practical relevance of these attacks. In this work, we present TEMPO-
Diffusion, a targeted
backdoor framework that localizes the malicious distribution shift to a temporal, in-distribution exposure. TEMPO-
Diffusion supports: (i) targeted attacks on and to specific classes, (ii) multiple sub-image
backdoors that reconstruct specific features within multiple, different output images and at multiple locations, and (iii) in-painting with time-conditioned triggers. To study relevant, practical security concerns in leveraging backdoored
diffusion models for synthetic training data, we also introduce CALISA: a balanced, region-aware traffic-sign
dataset emphasizing Canadian and U.S. road signs. Across CIFAR10, GTSRB, and CALISA, our experiments show that TEMPO-
Diffusion can reliably poison class-specific synthetic data generation and induce high attack success rates in downstream classifiers trained on that data.
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All authors William Aiken, Paula Branco, Guy-Vincent Jourdan, Iosif-Viorel Onut
Artificial Intelligence · Cross-listed from cs.IR
From Clicks to Intent: Cross-Platform Session Embeddings with LLM-Distilled Taxonomy for Financial Services Recommendations
Dianjing Fan, Yao Li, Kyaw Hpone Myint et al. (+4 more)
Sequential user behavior modeling is widely adopted in industrial recommender systems; however, significant gaps remain in financial services, where pre-login web interactions and authenticated in-app experiences differ drastically. Specifically, pre-login web users typically explore new products, whereas logged-in app users focus on account servicing. Due to the challenge of cross-channel entity resolution (e.g., matching anonymous web sessions to authenticated mobile accounts), web-based intent signals remain underutilized for post-authentication personalization. Existing methods for capturing web-based intent are often ad-hoc and narrow, lacking the flexibility to support both quantitative downstream recommendations and qualitative understanding at scale. In this work, we propose a scalable and dual-purpose intent prediction framework for web-based interactions and demonstrate its applicability for personalization. Our approach transforms raw web clickstreams into two outputs: a self-supervised
Transformer encodes multi-modal clickstreams into a compact session
embedding, while an LLM-based taxonomy generation and
distillation pipeline produces interpretable intent labels. Our system demonstrates that self-supervised clickstream representations combined with LLM-distilled taxonomies can jointly serve quantitative tasks and qualitative understanding in production: on the mobile homepage tile ranking task, the session
embedding improves macro Recall@1 by 1.88% and reduces Log Loss by 13.38% over production baselines. On the user conversion prediction task, the
embedding outperforms the LLM labels by 4.3% on micro F1, while the
distillation layer delivers interpretable labels at ultra-low latency with only a 7% performance drop.
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All authors Dianjing Fan, Yao Li, Kyaw Hpone Myint, Dwipam Katariya, Alexandre G.R. Day, Pranab Mohanty, Giri Iyengar
Artificial Intelligence
Accelerating Skill Assessment in Chess: A Drift-Diffusion-Enhanced Elo Rating System
Tianyuan Zhou, Zhizheng Fu and Tianming Yang
Rating systems such as Elo serve as the gold standard for matchmaking in competitive chess. However, they inherently suffer from response lag due to their exclusive reliance on match outcomes, neglecting the granular quality of gameplay. Nevertheless, incorporating move-by-move information into rating adjustments presents a significant challenge given the substantial noise and the vastness of the game-state space. To address this, we propose the Drift-
Diffusion-Enhanced Elo Rating System (DD-Elo), a novel skill assessment framework inspired by the drift
diffusion model (DDM) from cognitive neuroscience. By modeling skill expression as a decision-making process, our model integrates move-level data to capture rapid skill fluctuations. We provide a rigorous mathematical derivation proving that DD-Elo maintains a bounded deviation from the traditional Elo system, ensuring theoretical
alignment. Extensive experiments demonstrate that DD-Elo adapts to skill changes faster than Elo. Our findings suggest that DD-Elo offers an explainable, highly responsive, and backward-compatible solution for chess rating ecosystems. The implementation code is publicly available at https://github.com/Aquila-zhou1/DD-Elo .
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Artificial Intelligence · Cross-listed from cs.CV
A multi-task spatiotemporal deep neural network for predicting penetration depth and morphology in laser welding
Sen Li, Haichao Cui, Chendong Shao et al. (+2 more)
In
laser penetration welding, the assessment of penetration state and weld seam morphology plays a crucial role in determining the weld quality. This paper presents a comprehensive introduction of the innovative muti-task deep learning model that has the capability to predict penetration state, depth, and weld seam morphology with high accuracy. The monitoring platform relies on weld pool images captured during the
laser welding process using a complementary metal-
oxide-
semiconductor camera. The proposed model integrates spatiotemporal features extracted from top weld pool images along with welding parameters, establishing a deep learning framework based on convolutional neural networks and state space models for more efficient extraction and processing of spatial-temporal information. Furthermore, a reliable method for constructing the
dataset is proposed to enhance both robustness and generalization capability of the developed model. Validation results on the test set demonstrate that prediction accuracy for penetration state can reach 99.35%, while prediction error for penetration depth is 1.79 millimeter, and accuracy of reconstructing the weld cross-section is 95.65%. This study provides new insights and methodologies for in-situ quality control strategies in
laser penetration welding systems.
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All authors Sen Li, Haichao Cui, Chendong Shao, Yaqi Wang, Xinhua Tang
Artificial Intelligence · Cross-listed from cs.DL
Lacuna: A Research Map for Machine Learning
Martin Weiss, Miles Q. Li, Alejandro H. Artiles et al. (+4 more)
Lacuna is a research map for machine learning that uses LLMs to turn papers and scholarly metadata into markdown summaries, concept elements, research directions, and research proposals. Each item keeps links to the primary source records and papers that support it. We release the map with web, markdown, and MCP interfaces. Across LitSearch, Multi-XScience-CS/ML, and ScholarQA-CS-ML, Lacuna outperforms OpenScholar with the strongest gains on LitSearch
retrieval (Recall@10 0.538 vs. 0.424 for OpenScholar v3). We also evaluate Lacuna Deep Research, a multi-stage report
agent over the map, on 25 ReportBench-ML survey tasks: Lacuna Deep Research reaches 0.052 citation F1, 0.339 citation precision, 99 expert-reference hits, and 7.82/10 RACE report quality, while GPT-Researcher reaches 0.039 F1, 0.290 precision, 72 hits, and 5.24/10 RACE.
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All authors Martin Weiss, Miles Q. Li, Alejandro H. Artiles, Yacine Mkhinini, Chris Pal, Hugo Larochelle, Nasim Rahaman
Artificial Intelligence · Cross-listed from cs.CR
CyberChainBench: Can AI Agents Secure Smart Contracts Against Real-World On-Chain Vulnerabilities?
Jintao Huang, Fengqing Jiang, Radha Poovendran et al. (+1 more)
We present CyberChainBench, a
benchmark for evaluating LLM-based
agents on smart contract security across three complementary tasks: vulnerability detection, exploit generation, and patch synthesis. Built from 541 real-world exploit incidents from DeFiHackLabs spanning 9 EVM chains, the
benchmark provides end-to-end on-chain evaluation where
agents interact with historical blockchain state through isolated evaluation environments orchestrated by Harbor, using tools to read code, trace transactions, and validate exploits on mainnet forks. Each case is anchored to a specific block and includes structured ground truth covering vulnerability type, localization, and attacker profit. Exploits are graded by economic impact on historical forks; patches are validated by replaying historical attacks and legitimate transactions as fail-to-pass test oracles on a proxy-upgradeable subset. We define a five-type vulnerability taxonomy and evaluate multiple
agent–model configurations. Results reveal a clear difficulty gradient: the best configuration scores 37.5% on detection, 43.7% on exploitation, but only 23.4% on patching, with the top
agent (Codex with GPT-5.5) realizing \$57.4M in total exploit profit across the 200-case exploit set at a cost of $2.39 per case.
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All authors Jintao Huang, Fengqing Jiang, Radha Poovendran, Zhiqiang Lin