Artificial Intelligence · Cross-listed from cs.AR
HYDRA: A Heterogeneous Chiplet DSE Framework for Serving Dynamic Hybrid LLM Workloads
Jiahao Lin, Alish Kanani, Sangwan Lee et al. (+2 more)
Hybrid
Transformer-Mamba large
language models (LLMs) enhance long-context efficiency, but their heterogeneous computation and communication patterns complicate efficient hardware acceleration. Chiplet-based architectures offer a scalable solution by integrating specialized compute and
memory units. However, the design space spanning static architectural configurations and dynamic runtime policies is prohibitively large to explore exhaustively. To address this challenge, we present HYDRA, a comprehensive design space exploration framework for hybrid LLM serving on heterogeneous chiplet systems. HYDRA jointly explores chiplet composition, placement, inter-chiplet bandwidth provisioning, dynamic batching, and runtime scheduling. It integrates communication-aware placement, dynamic batching, elastic task scheduling, and a fast Markov-based performance
estimator that captures multi-tenant runtime dynamics for efficient and accurate exploration. Across all workloads, HYDRA delivers 1.55x the throughput and 43.7 percent lower time-to-first-
token on average, with throughput gains reaching up to 2.3x compared to state-of-the-art baselines. These results highlight that co-designing architecture and runtime policies is critical for efficient large-scale LLM serving on heterogeneous chiplet systems.
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All authors Jiahao Lin, Alish Kanani, Sangwan Lee, Jaehyun Park, Umit Ogras
Artificial Intelligence · Cross-listed from q-fin.TR
Concentrated Liquidity Provision: a Reinforcement Learning Perspective
Georgios Chionas, Charalampos Kleitsikas, Stefanos Leonardos et al. (+2 more)
Automated
market makers (AMMs) are a cornerstone of decentralised finance (DeFi). Constant product
markets with concentrated liquidity, such as UniswapV3, are now a well-established design. In these
markets, liquidity providers (LPs) face a sequential decision problem: they must decide when to rebalance their positions and which price ranges to allocate capital to as
market conditions evolve. We formulate dynamic liquidity provision as a
stochastic impulse control problem and use reinforcement learning (RL) to solve it, focusing on providing interpretable solutions. We show that learned policies exhibit rich state-dependent behaviour, allocating liquidity according to mispricing, rebalancing costs, uncertainty, inventory exposure, and heterogeneous risk preferences. These behaviours help compress the left tail of the Profit and Loss (PnL) distribution and avoid catastrophic outcomes under high uncertainty. Finally, we
benchmark the RL
agents against baseline and sophisticated
agents from the AMM microstructure literature and analyse their performance.
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All authors Georgios Chionas, Charalampos Kleitsikas, Stefanos Leonardos, Leandro Sánchez-Betancourt, Carmine Ventre
Artificial Intelligence · Cross-listed from cs.SI
Fairness-Aware Network Embeddings: Methods, Applications, and Challenges
Ella Has, Harshith Kumar Yadav, Gaurav Dixit et al. (+2 more)
Network
embedding methods learn low-dimensional representations of graph-structured data to support downstream tasks such as node classification, link prediction, and influence maximization. However, real-world networks often reflect structural inequalities arising from demographic imbalances, homophily, and other societal biases, which
fairness-agnostic
embedding methods can encode and amplify. To address this issue, numerous
fairness-aware network
embedding methods have been proposed to mitigate bias while preserving
embedding utility. This survey presents a comprehensive overview of
fairness-aware network
embeddings for complex networks. We propose a taxonomy that categorizes existing methods along three main complementary dimensions: underlying
embedding approach (spectral, random walk, graph neural network,
Bayesian, and method-agnostic),
fairness intervention strategy (pre-processing, in-processing, and post-processing), and
fairness objective criterion (
embedding- or task-level). We further compare methods with respect to group versus individual
fairness and assumptions regarding sensitive attributes. Finally, we discuss current limitations and highlight promising future research directions. This survey provides a unified perspective on
fairness-aware network
embedding and serves as a reference for developing fair and trustworthy network representation learning methods.
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All authors Ella Has, Harshith Kumar Yadav, Gaurav Dixit, Mykola Pechenizkiy, Akrati Saxena
Artificial Intelligence · Cross-listed from cs.CV
Does Marginal Coverage Guarantee Class-Conditional Safety for Zero-Shot VLMs Under Shift?
Jai Kumar Sharma and Amartya Dutta
Split-conformal prediction provides marginal coverage under exchangeability and is increasingly used as an abstention layer for zero-shot
vision-
language models (VLMs). We audit this practice under deployment shift for CLIP, OpenCLIP, and SigLIP across ImageNet and non-ImageNet settings. Marginal coverage can remain relatively high while class-conditional tail coverage collapses: on ImageNet-Sketch, worst-class coverage falls to $\approx 0$ and 10-12% of classes lie below a finite-sample null floor, despite marginal coverage of about 0.86. The failure is aligned with target-domain class accuracy but is not predicted by the source-domain diagnostics we test. Source-side Mondrian calibration improves the in-distribution tail but does not transfer, while clustered conformal and Conf-OT improve marginal or average metrics without recovering the worst-class tail. Target-side class calibration substantially lifts the tail, but requires labels for every class and remains set-size-intensive. We further identify a 2-3$\times$ cross-family efficiency gap and show that native SigLIP sigmoid scores remove APS's probability-mass interpretation. The findings persist across the tested model scale, pretraining corpus,
prompt, miscoverage level $\alpha$, and shifted non-ImageNet settings. Marginal conformal coverage should therefore be treated as an average reliability statistic, not as a safety guarantee for the class tail.
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Artificial Intelligence · Cross-listed from cs.LG
Mechanistic Tomography: Designed Measurement for Control-Oriented Interpretability
Vijay Erramilli
Mechanistic interpretability seeks quantities that models do not expose directly: represented states, component effects, interactions, and responses to interventions. Patching, gradients, Hessian-vector products, and subset interventions provide different measurements under different access assumptions and may target different quantities. We formulate their shared measurement structure as mechanistic
tomography: designed measurement for recovering internal mechanisms and intervention effects. For a chosen basis and intervention family, measurements take the form y = Ax + w, where A describes the interventions, x is the target map, and w contains nonlinear response, sampling error, and basis misspecification. This
language gives a practical procedure: start with the least costly measurements, test on held-out interventions at the intended scale, calibrate simple mismatch, and expand the measurement family when structured residuals remain. Control provides a demanding validation setting because an estimate that guides an intervention acts as an observer. In a two-HMM model, control error rises with observer error, while target improvement can hide nuisance-state movement. Under forward-only access, sparse aggregate measurements recover a finite-effect map with fewer interventions than coordinate patching. With gradient access, finite probes improve a local attribution map. Lifted measurements and Hessian-vector products recover interactions missed by first-order maps, while Tracr shows that the required family depends on the basis. On GPT-2-small IOI, the Name Mover-Negative Name Mover interaction is the largest held-out predictive term among three tested cross-group pairs. On Qwen-2.5-7B, finite calibration makes an additive refusal-response map adequate, so held-out error does not support pairwise lifting.
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Artificial Intelligence · Cross-listed from cs.LG
Improved Confidence Estimates for Black-Box Large Language Models
Sokhna Diarra Mbacke, Mouloud Belbahri and Gabriel Loaiza-Ganem
Uncertainty quantification (UQ) is essential for the safe deployment of large
language models (LLMs). Existing methods, from verbalized confidence to ones requiring multiple generations, are often zero-shot and produce scores quantifying uncertainty without the need for labelled data. Nonetheless, in practice one must always evaluate their performance on a
dataset of interest before deployment. In this work we show that, by leveraging this
dataset, we consistently outperform these existing scores. Specifically, we build simple classifiers that predict LLM response correctness by using these scores and the correctness of similar queries as features. Our method produces minimal computational overhead, making it a cheap and straightforward enhancement for UQ in LLMs for real-world applications.
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Artificial Intelligence · Cross-listed from cs.LG
Quantum Kernel Estimation for the Discovery of Early Lung Cancer Detection
Hamed Javidi, Alex Zajichek, Hakan Doga et al. (+3 more)
Lung cancer screening with low-dose chest computed
tomography reduces mortality, but its impact is limited by uptake, adherence, and management challenges. Blood-based
cell-free DNA (cfDNA) biomarkers offer a complementary approach, although early detection remains difficult because of lung cancer heterogeneity and high-dimensional, nonlinear molecular signals. We evaluated
quantum-classical hybrid machine learning for lung cancer detection using DNA fragmentomics and DNA methylation. After feature selection, models were trained using 20- and 40-feature subsets. Features were encoded into
quantum Hilbert space using angle and dense-angle feature maps with multiple
entanglement strategies. Fidelity-based
quantum kernels were computed with exact statevector simulation and integrated with precomputed-kernel SVM and kernel-PCA logistic regression and compared with an SVM model trained on the original features. This framework enabled systematic evaluation of how encoding and
entanglement design affect classification. Across repeated held-out evaluations,
quantum-kernel models achieved competitive performance on both
datasets. For fragmentomics, several 20-feature configurations improved AUC relative to a classical SVM baseline, suggesting effective capture of nonlinear cfDNA fragmentation structure. For methylation, the classical SVM achieved the highest AUC, although selected
quantum models remained competitive and improved specificity in some cases. Increasing features from 20 to 40 did not consistently improve performance and often increased variability. Overall, these results support
quantum kernel methods as a promising approach for cfDNA-based lung cancer detection.
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All authors Hamed Javidi, Alex Zajichek, Hakan Doga, Laxmi Parida, Filippo Utro, Peter J. Mazzone
Artificial Intelligence
Outcome Monitors: Recovery Affordances for Silent Tool Failures
Sugam Panthi and Rabab Abdelfattah
When a tool call times out, the
agent sees the failure and can route around it. A cached error page or negative price can instead arrive in the expected format and be consumed as fact. We introduce Outcome Monitors, which detect violations of outcome contracts mined from task-disjoint traces or derived from public schemas. On a violation, the monitor preserves the result and issues a nonbinding receipt naming the violated property and public recovery tools. In frozen, prespecified evaluations with injected failures, Outcome Monitors raise ToolMaze completion from 10.9% to 28.1% across four models in two provider families and replicate in a third. In tau-bench retail, completion improves by 14.0 and 12.0 points on two tiers. In separate ToolMaze controls, removing the recovery-tool list eliminates the measured gain and restoring it recovers the effect; diagnostic detail and timing produce no detectable differences. Gains concentrate where the fault blocks completion. On a suite transcribed from a published incident taxonomy, detection outside the mined vocabulary falls to 46%, though delivery continues and completion is unchanged. Recovery tools are the active receipt content in these controls; extending detection beyond the contract vocabulary remains open.
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Artificial Intelligence
Air Traffic Control Using Large Language Models: Prompt Engineering, Architecture, and Evaluation
Mahyar Ghazanfari, Matthias Casanova, Jordan Kam et al. (+4 more)
Air traffic control (ATC) communication is a safety-critical dialogue that remains largely human-driven even as other parts of air traffic management have been semi-automated. In this article, we experimentally evaluate whether large
language models (LLMs) can generate operationally realistic ATC transmissions. An experimental general-aviation flight flying over the San Francisco "Bay Tour" route is hand-transcribed and used as ground truth (P0). Through a pilot-in-the-loop process we design five
prompt structures (P1-P5) of increasing constraint and embed them in a stateful multi-turn pipeline, where the model plays ATC to a fixed pilot transcript while conditioning on the accumulating dialogue history. Across nine open- and closed-source LLMs we vary the
prompt, the presence of a worked transcript from a different experimental flight as an in-context example, and whether the model conditions on its own prior replies or on injected ground-truth history. Turns are scored with lexical, structural, and semantic similarity metrics and by an LLM-as-judge (GPT-5.5) validated against human expert annotation. Supplying a worked example improves similarity, but tightening the
prompt does not: the lightest
prompts perform best and the most heavily scripted one collapses as its own errors accumulate through the dialogue, which injecting correct history repairs. These results outline a concrete path and its current limits toward LLM-assisted ATC.
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All authors Mahyar Ghazanfari, Matthias Casanova, Jordan Kam, Alex Zongo, Peng Wei, Torsten Darrell, Alexandre Bayen
Artificial Intelligence · Cross-listed from cs.CL
SPADE: Self-Play in Adaptive Synthetic Executable Environments
Bo Liu, Simon Yu, Yiding Jiang et al. (+15 more)
Continuous self-improvement requires an ever-expanding pool of self-generated, diverse, adaptive goals. For
language agents, existing training environment pools (hand-curated, statically synthesized, or frozen-verifier) keep the goal distribution fixed as the learner scales. We introduce SPADE (Self-Play in Adaptive Synthetic Executable Environments), a self-play RL framework in which a single LLM plays two roles: an Environment Designer that writes complete, long-horizon training environments as executable code with an OpenAI Gym-style reset()/step() interface, and a
Reasoning Agent that learns to act in them. Each is a stateful, multi-turn environment (state transitions, reward functions, and verification code), so one interface spans
reasoning problems and multi-step agentic tool use. The
Reasoning Agent's regret is estimated using the gap between its reward with and without privileged hints; in optimizing this regret signal the Environment Designer learns to target environments at the edge of the
agent's capabilities while keeping them feasible. Through extensive experimentation, we find several components critical to success: grounding the Environment Designer on documents sampled from a large pretraining corpus, and giving it an accumulated environment
memory. Scaling to 30B-parameter models, SPADE improves over the strongest fixed-environment baseline by +5.3 on average across eight held-out math, science, code, and
reasoning benchmarks, and lifts the tool-use setting by +5.7 on BFCL-v4 multi-turn and +13.9 on ACEBench-
Agent; on the games setting, the margin over the strongest baseline grows with model scale. By making environment design itself a learnable component, SPADE takes a concrete step toward open-ended self-improvement.
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All authors Bo Liu, Simon Yu, Yiding Jiang, Ao Qu, Andrew Zhao, Zichen Liu, Junsu Kim, Zijian Zhou, Seungone Kim, Tongzheng Ren, Mickel Liu, Hanfei Yu, Zhaorun Chen, Weiyan Shi, Paul Pu Liang, Luke Zettlemoyer, Yejin Choi, Natasha Jaques
Artificial Intelligence · Cross-listed from cs.RO
ADEPT: Accelerating Dexterity via Pre-Training and Post-Training using Reinforcement Learning
Jayjun Lee, Jessica Yin, Asif Rana et al. (+8 more)
We introduce Accelerating Dexterity via Pre-Training (ADEPT), a large-scale reinforcement learning (RL) framework for learning sim-to-real transferable dexterity across high degree-of-freedom (DoF)
robot embodiments that can solve long-horizon tasks directly from raw visuo-tactile perception. ADEPT pretrains a dexterous policy on a generic object reposing task, then post-trains downstream policies with this pretrained behavior as a prior. ADEPT enables learning new behaviors that are otherwise difficult to discover from scratch on multi-fingered
robots and avoids learning the same set of skills over again for every new downstream task. The pretrained policy zero-shots the reposing phase of downstream tasks, but naïve RL fine-tuning rapidly degrades this capability during transfer. We address this with a stable post-training recipe combining behavior-cloning
distillation, critic warm-up, and conservative on-policy updates. To safely exploit the full kinematic dexterity, we introduce a joint-space
Geometric Fabric that mediates between the RL policy and the
robot. We distill post-trained teachers into perceptive students that zero-shot sim-to-real transfer on two embodiments: a 23 DoF Kuka-Allegro with two RGB cameras, and a 29 DoF Flexiv-Sharpa with two RGB cameras and five
vision-based tactile sensors, and can solve long-horizon tasks from challenging initial states with dexterity at human-level speed.
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All authors Jayjun Lee, Jessica Yin, Asif Rana, Nicholas Blauch, Octi Zhang, Sam Mady, Mohak Bhardwaj, Nima Fazeli, Nathan Ratliff, Karl Van Wyk, Ankur Handa
Artificial Intelligence · Cross-listed from cs.LG
Beyond Teacher Likelihood: Group-Calibrated On-Policy Distillation for Long-Context Reasoning
Zhu Zhang, Jixun Wang, Xiaoang Xu et al. (+6 more)
On-policy
distillation (OPD) trains a student on its own responses using dense
token-level guidance from a stronger teacher. In long-context tasks, however,
token-level teacher support can favor locally plausible responses that omit evidence distributed across the input or violate global task constraints. Task-specific verifiers, in contrast, evaluate task completion at the response level and may return graded rewards that reflect partial success. We diagnose this mismatch on fixed responses from two representative long-context evidence-aggregation tasks. Across longer input ranges, trajectory-level OPD scores become progressively less aligned with verifier rewards, indicating teacher-verifier disagreement. Motivated by this observation, we introduce Group-Calibrated On-Policy
Distillation (GC-OPD). GC-OPD separately normalizes verifier rewards and trajectory-level OPD scores within each rollout group and uses their difference as a signed teacher-verifier disagreement residual. Relative-advantage-based credit assignment (RACA) distributes this trajectory-level residual across
tokens according to their relative OPD advantages while preserving the original OPD signal. Across five long-context
benchmarks, post-training with GC-OPD raises the five-
benchmark averages of the official Qwen3-4B and Qwen3-8B checkpoints from 29.08 to 40.47 and from 35.12 to 44.65, respectively. Vanilla OPD reaches 39.31 and 43.56 under the same setup. Controlled ablations show that the signed residual is more effective than either an additional OPD-derived term or direct group-normalized verifier reward addition, while RACA further improves over uniform
token allocation. Together, these results demonstrate that group-relative residual calibration can incorporate verifier outcomes without discarding dense
token-level guidance. Code is available at https://github.com/SolereZhang/GC-OPD.
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All authors Zhu Zhang, Jixun Wang, Xiaoang Xu, Xiaorong Wang, Zihan Zhou, Zhiyuan Wang, Shuo Wang, Chaojun Xiao, Yuezhi Zhou
Artificial Intelligence · Cross-listed from cs.SD
Finetuning Strategies for Querying Sounds by Vocal Imitation
Aditya Bhattacharjee, Christos Plachouras, Sungkyun Chang et al. (+1 more)
This technical report describes our winning submission to the AES AIMLA 2025 Challenge on querying sound effects by vocal imitation. We investigate two complementary fine-tuning strategies: contrastive learning with a frozen, pretrained CED encoder, and joint contrastive-triplet learning with semi-hard negatives using a MobileNetV3 encoder. This report has been updated for posterity to include details released after the challenge.
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All authors Aditya Bhattacharjee, Christos Plachouras, Sungkyun Chang, Emmanouil Benetos
Artificial Intelligence · Cross-listed from physics.ao-ph
Interpretable AI predicts a 2026 summer dry anomaly in central China
Anran Wang, Wen Shi, Yong Luo et al. (+5 more)
Seasonal precipitation anomalies are largely regulated by atmospheric circulation, which dynamical models predict with greater reliability than precipitation itself. Here, we employ a deep learning model that translates dynamical circulation predictions into precipitation estimates. Predictions initialized from March to May consistently indicate a dry anomaly over central China in summer 2026. Retrospective evaluations revealed higher predictive skill in the analogue years, which also tended to feature central equatorial Pacific warming persisting from the preceding winter into summer. This warming favors an anomalous cyclonic circulation over the western North Pacific-South China Sea-South China region, which induces northerly winds and moisture divergence that jointly suppress rainfall over central China. Supporting this mechanism, layer-wise relevance propagation (LRP) independently identifies these northerly winds as the dominant driver of the prediction among all model inputs. Perturbation tests supported this attribution: removing LRP-identified features effectively eliminates the dry anomaly. Our framework thus provides physically interpretable explanations for AI-derived regional
climate projections, facilitating evidence-based assessment before observational data become available.
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All authors Anran Wang, Wen Shi, Yong Luo, Jianbin Huang, Lijuan Chen, Junhu Zhao, Weixin Jin, Huihui Yuan
Artificial Intelligence
Beyond the Transcript: Detecting Covert Co ordination in Latent Multi-Agent Communication
Ramneet Kaur, Pradyumna Chari, Ramesh Raskar et al. (+3 more)
Language-model
agents can communicate through continuous hidden states that are invisible in public transcripts, creating opportunities for covert harmful coordination. We introduce Verifiable Latent
Alignments (VLA), an activation-aware framework for monitoring and steering these private communication channels. For every monitored decision, VLA links the private latent-state record and channel status to the resulting public action using a shared event identifier, enabling matched causal analysis. Our first contribution is a neutral-only three-layer monitor combining representation anomaly detection, counterfactual action-distribution influence, and sparse-autoencoder interpretation support. Our second contribution is a steerability framework spanning black-box behavioral instructions and white-box matched-neutral counterfactuals. Our third contribution is an evaluation on a controlled multi-
agent auction
benchmark covering homogeneous and heterogeneous model pairs, many-
agent scalability, and intervention effectiveness. The sequential monitor achieves mean area under the receiver operating characteristic curve (AUROC) of 0.993 for homogeneous
agents and 0.854 for heterogeneous pairs when text- and latent-collusion rows are pooled as positives. In Qwen3-0.6B auctions with 25-100 bidders, monitoring requires only a small normalized load relative to all possible directed pairs, while full white-box steering achieves 100% bid-distribution recovery and reduces collusive low-bid behavior by 47.3 percentage points. Because full white-box steering replays the matched neutral counterfactual, its exact recovery is a sanity check by construction. Overall, the controlled study shows that the evaluated private channel attacks can be monitored without training the primary monitor on attack examples and mitigated when matched counterfactual access is available.
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All authors Ramneet Kaur, Pradyumna Chari, Ramesh Raskar, Jugad Singh, Sumit Kumar Jha, Anirban Roy
Artificial Intelligence · Cross-listed from cs.DC
Pre-Compiled Pipeline Shards for Distributed LLM Inference on Intel AI PC Fleets
Tate Berenbaum and Muthaiah Venkatachalam
Modern Intel AI PCs ship capable integrated GPUs and NPUs with 16+ GB of unified
memory, and they spend considerable time idle. That is not enough
memory to fit a large model such as a 70B-parameter LLM. We show that a handful of AIPCs, working together over an ordinary network, can serve models beyond the capability of any single one. We use pipeline parallelism: a model is split by layer into per-stage shards, each pre-compiled into an OpenVINO graph, so that every machine runs one shard and passes activations to the next. Three techniques make this fast enough to be useful. First, we recover the speed of the unsplit model: a naive per-stage export runs well below monolithic inference because it misses an OpenVINO GPU optimization, and injecting a beam_idx Gather into each shard triggers that optimization (the IndirectKVCache fusion) and brings the shards to parity. Second, we leverage speculative decoding on stateful OpenVINO models. Third, the pipeline serves several users at once by interleaving their requests across the stages, each request carrying its own cache (micro-batching). Together, a two-node Llama 3.1 8B INT4 pipeline serves two concurrent users at 1.79x the single-user throughput of the unsplit model on the same hardware, and the gap widens under simulated wide-area latency. The same design scales to a 70B model that no single fleet member can hold: a four-node deployment of Lunar Lake AI PCs on Intel Tiber Cloud serves a single user at interactive speed, with output
token-for-
token identical to the same four-node pipeline decoding without speculation. Code, raw
benchmark logs, and reproduction scripts ship as a self-contained package at https://github.com/labscommunity/pipeline-sharded-inference-paper (in the top-level reproduction/ directory).
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Artificial Intelligence
Grouping the Stochastic Machine: Precision, Not Capability, as the Frontier Metric for AI Systems
George Andrikopoulos
Frontier
language models are compared, marketed, and benchmarked on capability – what their best or average output can achieve. I argue this measures the wrong axis. The models have saturated accuracy: their mean output lands on the target. What now separates one system from another in practice is precision: how tightly concentrated their outputs are around that target across repeated, identical requests. Borrowing the marksman's distinction, capability is where the average shot lands; reliability is the size of the group. I make three claims. First, precision, not capability, is the frontier differentiator between systems, and
benchmark culture systematically fails to measure it, reporting central tendency rather than spread. Second, precision is measurable, cheaply and without circularity, by running a fixed suite of deterministically scored tasks many times at fixed temperature and computing the per-task consistency of outcomes – no model-in-the-loop grader required. Third, the measurement is not merely descriptive but decision-guiding: it separates consistent failures (a tight group off-centre, correctable by the operating discipline of Paper 1 – a sight adjustment) from scattered failures (a wide group, correctable only by changing the model or its sampling – a rifle problem). I define a grouping metric, specify a harness, and show how tracking a human-AI pair's grouping over time yields the compounding signal that Paper 1's field study requires. A first real run, since replicated, illustrates both the method and its most important limit: one measured gap was closed completely by a single rule (0/5 -> 5/5), while a suite of tasks authored from the rules themselves found no value, because a frontier model already embodies explicit good practice – establishing that a discipline's worth is found by measurement on real work, not constructed from its own rulebook.
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Artificial Intelligence · Cross-listed from cs.LG
Leaf Values as Coordinates: Exact Contrastive Explanation for Gradient-Boosted Ensembles
Emanuele Luzio
A gradient-boosted ensemble predicts by summing one leaf value per tree. Read those values as coordinates rather than as intermediate results, and every instance becomes a point in R^M on which the model acts linearly: the score is the sum of the coordinates. This small change of view makes contrastive explanation exact. The difference between two instances is a vector that is identically zero wherever they share a leaf, so the gap between a rejected applicant and an accepted one is carried by a handful of coordinates, each traceable to a real split in a real tree. Nothing is fitted, sampled, or assumed additive in features – the additivity is already there, in the right space. We build a recourse method on this representation and evaluate it on five tabular
datasets under repeated cross-validation. Its recommendation reconstructs the model's own decision to 6.2 x 10^-15, so an auditor can re-check the arithmetic without the model. On the credit
datasets it is Pareto-non-dominated on effort against realism. And when recommendations are restricted to changes the subject could actually make – not their age, not a settled delinquency – it retains 58% of its validity where the strongest baseline retains 41%, a distinction the standard evaluation cannot see because it never asks whether a recommendation can be carried out.
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Artificial Intelligence
Tuning the Stochastic Machine: A Systems Engineer's Operating Model for Human-AI Engineering
George Andrikopoulos
When an expert corrects an LLM assistant's error, the correction usually dies with the session, and the error class returns. I argue this is an operations problem, not a tooling problem: mechanisms for persisting corrections exist and are shipping, but the discipline for governing them – versioning with provenance, recurrence monitoring, counter-metrics, retirement of stale rules – does not. Writing as a systems engineer of thirty years, I map the LLM stack onto the machines my profession already operates (frozen silicon, firmware, loadable modules, persistent configuration, volatile
memory), identify where the mapping fails (
stochastic generation, configuration that binds only probabilistically, no general-purpose retirement (verification) stage by default), and derive from the failures a seven-principle operating discipline with an error loop at its core. Three cases from my own practice illustrate the mechanism, among them a control that silently became the exact harm it was built to prevent. I close with the measurement framework this view implies and the lab study required to test it.
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Artificial Intelligence · Cross-listed from cs.LG
PGFS++: Molecular Property Improvement under Synthesis and Diversity Constraints
Boqiao Zhang, Godbless James, Sai Krishna Gottipati et al. (+1 more)
Improving molecular properties, such as drug-likeness or binding affinity, is a recurring task in early-stage drug discovery. However, molecules optimized in an unconstrained chemical space have limited practical value if they cannot be synthesized. Policy Gradient for Forward Synthesis (PGFS) is a synthesis-aware reinforcement learning method for molecular improvement, but its use of reactant
embedding prediction makes reactant selection indirect, which, as we show, limits learning effectiveness. We first develop PGFS+, in which reaction templates and second reactants are represented by trainable
embedding lookup tables. Combined with a more effective scoring function and RL
algorithm, PGFS+ significantly improves the desired property. However, it exposes a reward-hacking failure mode: a powerful reactant search can map diverse input molecules to the same high-reward magnet molecule, improving the reward while collapsing the output diversity. We therefore introduce PGFS++, a synthesis-aware reinforcement learning framework for input-specific molecular improvement. Given an input molecule, PGFS++ treats it as the start of a forward-synthesis trajectory, applies learned reaction templates with compatible in-stock building blocks, and produces a molecule with improved target properties, an explicit synthesis route, and structural similarity to the input. Experiments on molecular improvement tasks show that PGFS++ improves target properties while preserving high output diversity.
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All authors Boqiao Zhang, Godbless James, Sai Krishna Gottipati, Andrew Fitzgibbon
Artificial Intelligence · Cross-listed from cs.LG
Discretizing Continuous Time Series for Imputation with Masked Diffusion Training
Dongbin Kim, Seungyun Lee, Geonwoo Shin et al. (+1 more)
Time series imputation is a crucial area for reliable time series analysis, yet it remains challenging due to the complex temporal dynamics and noise of real-world data. Existing approaches, however, exhibit two limitations: missing and observed values are embedded within the same representation space without explicit structural separation, and continuous
diffusion-based methods are trained to predict added noise rather than the original signal. To address these, we propose the Masked
Diffusion Time-series Imputation Model (MDTIM), which leverages the training paradigm of masked
diffusion model for imputation tasks. The MASK
token is structurally orthogonal to valid observations, and the model directly predicts the original values, naturally aligning both the representation and the learning objective with the imputation task. To bridge the gap between discrete masked
diffusion and the continuous, ordinal nature of time series, we further introduce
Stochastic Discretization, which maps continuous values to ordinal-aware
tokens while preserving continuous dynamics. Our experiments on diverse
benchmarks confirm that MDTIM achieves superior robustness and scalability, consistently outperforming state-of-the-art deterministic and generative baselines across various missing scenarios.
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All authors Dongbin Kim, Seungyun Lee, Geonwoo Shin, Jaewook Lee
Artificial Intelligence · Cross-listed from cs.LG
Open-MOPD: Diagnosing and Fixing Capability Imbalance in Multi-Teacher On-Policy Distillation
Huan-ang Gao, Haohan Chi, Yong Yan et al. (+7 more)
Multi-teacher on-policy
distillation (M-OPD) has emerged as a promising paradigm for consolidating domain-specialized reinforcement learning (RL) experts into a single generalist student via dense,
token-level reward supervision. Despite its practical success, the optimization dynamics governing multi-teacher capability integration remain poorly understood, and open, rigorously reproducible recipes are conspicuously lacking. In this work, we establish a controlled M-OPD
benchmark on SmolLM3-3B-Base with oracle routing, isolating capability integration from routing ambiguity. Our investigation reveals a pronounced capability integration gap: standard M-OPD captures only 35.6% of the available headroom relative to a domain-routed oracle ensemble, with concise tasks such as instruction following suffering severe degradation and premature stagnation. Crucially, we show that this failure stems not from gradient conflict, but from a severe misallocation of the
token-level optimization budget. This pathology is driven by three orthogonal factors: structural sequence-length disparities across domains, dynamic convergence drift due to non-uniform learning rates, and multi-step reward staleness from asynchronous policy updates. To resolve these imbalances, we introduce Open-MOPD, a principled framework incorporating
token-share balancing, gap-aware dynamic budget allocation, and student reward refresh. Together, these mechanisms systematically restore cross-domain balance, elevating headroom recovery from 35.6% to 83.4% in a single deployable student. We fully open-source our end-to-end post-training recipe, training trajectories, and evaluation suites on an academically accessible hardware budget.
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All authors Huan-ang Gao, Haohan Chi, Yong Yan, Shiyuan Feng, Hanlin Wu, Zheng Jiang, Bingxiang He, Wei-Ying Ma, Ya-Qin Zhang, Hao Zhou
Artificial Intelligence · Cross-listed from cs.CV
Detecting Backdoors in Object Detection via Pre-NMS Prediction Distribution Shift
Longtian Wang, Zhengyu Zhao, Chenhao Lin et al. (+5 more)
Object detection models deployed in safety-critical applications remain vulnerable to
backdoor attacks that cause targeted misbehaviors when a hidden trigger is present. Existing detection methods either rely on trigger inversion or exploit architecture-specific assumptions, and critically, representative existing methods fail to generalize reliably to scene-level attacks, where a single trigger induces anomalous behavior across all objects in the scene simultaneously. We present DistScan, a
backdoor detection framework based on a simple but previously unexploited observation:
backdoor injection systematically shifts a model's pre-NMS prediction class distribution away from its training class frequencies, even on clean inputs without any trigger present. DistScan aggregates intermediate class predictions over a clean validation set and flags a model as backdoored if the resulting distribution deviates significantly from the training class frequencies, requiring no model weight access, no trigger knowledge, and no additional training. Extensive experiments on MS-COCO and PASCAL VOC across two architectures and three scene-level attack scenarios demonstrate that DistScan substantially outperforms existing methods, improving average detection accuracy over the best-performing applicable baseline by 27.32 percentage points.
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All authors Longtian Wang, Zhengyu Zhao, Chenhao Lin, Le Yang, Shiwei Wang, Yuhan Zhi, Xiaofei Xie, Chao Shen
Artificial Intelligence · Cross-listed from cs.RO
DA-WAM: Decision-Aligned Future Latents for Driving World Models
Ruiguo Zhong, Xiaolong Chen, Benshan Ma et al. (+5 more)
World models for autonomous driving should anticipate the consequences that matter for driving decisions rather than reconstruct every detail of future scenes. However, predictive representations alone are not guaranteed to distinguish candidate actions that lead to different planning outcomes.We propose DA-WAM, a decision-aligned world action model that couples a Joint-
Embedding Predictive Architecture (JEPA) with candidate-level trajectory scoring. During joint training, the JEPA objective grounds future representations in observed scene dynamics, while the planning objectives encourage these representations to capture distinctions relevant to safety and utility. An action-conditioned predictor generates a distinct future latent for each candidate trajectory, and a multi-head trajectory scorer uses the corresponding latent to estimate planning factors and overall utility. In addition, safety-critical post-training with hard negatives further improves discrimination between geometrically similar trajectories with different safety outcomes. DA-WAM achieves state-of-the-art performance among learned camera-based planners on NAVSIM-v1, NAVSIM-v2, and Bench2Drive. Ablation studies demonstrate the effectiveness of each component in driving decision-making.
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All authors Ruiguo Zhong, Xiaolong Chen, Benshan Ma, Lang Zhang, Mingyue Feng, Yaonong Wang, Pei Liu, Jun Ma
Artificial Intelligence · Cross-listed from cs.CV
ReWEIGH the Evidence: Calibrating Token-Level Ordinal Visual Evidence to Mitigate Hallucinations in Large Vision-Language Models
Jihae Jeong, Junha Choi and Hwanjo Yu
Large
vision-
language models (LVLMs) often hallucinate, generating content that the input image does not support. Preventing such content during decoding calls for a candidate-specific measure of how strongly the image supports the
token under consideration. The model's visual-
token states offer a natural source of this evidence because projecting each state through the output head reveals which vocabulary items that position favors. These position-wise readouts cannot be pooled directly because their probability magnitudes are not comparable across visual positions. Vocabulary ranks provide a scale-invariant basis for pooling, but
tokens still differ systematically in their typical rank-based evidence. We propose ReWEIGH, a training-free decoding intervention that aggregates these ranks across visual positions and compares each candidate with a
token-specific reference estimated from unlabeled images. At inference, ReWEIGH caches the image evidence during prefill and applies a bounded penalty only to candidates that fall below their reference. On four 7B backbones, ReWEIGH reduces hallucinated object mentions by up to 21.3% while largely preserving or improving descriptive and general performance. With evidence cached, the average added latency is 1.33% per
token, and the reductions extend across six architecture families to 32B parameters.
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Artificial Intelligence
Robust Risk Under Evolving Uncertainty: A Wasserstein Counterpart of the Entropic Value-at-Risk
Deep Kumar Ganguly and Jan Křetínský
An
agent still learning its environment should be cautious while ignorant and bold once confident. The entropic value-at-risk captures this through a robust-optimization identity—a confidence level fixes the radius of a relative-
entropy ball of alternative models—but that ball cannot reach catastrophes the nominal deems impossible, precisely what a safe
agent must hedge. We instead use an optimal-transport ball and study the coherent risk measure it induces, the Wasserstein entropic value-at-risk. It has a variational dual mirroring the entropic formula (an inverse temperature becomes a transport price), occupies a definite place in the risk hierarchy, and provably accounts for the reachable catastrophes the entropic measure ignores; we verify both dualities numerically. Driving the transport radius by belief
entropy then yields a closed-form robust dynamic-programming operator whose caution contracts as the belief sharpens, with a certified safety sandwich and a sharp safety switch.
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Artificial Intelligence
What is Missing from AI Post-Training AI: An Empirical Analysis
Joy Jia Yin Lim, Xin Huang, Hao Peng et al. (+5 more)
Large
language model (LLM)
agents can now post-train an LLM end-to-end, raising the prospect of recursive self-improvement (RSI). Yet this progress is measured by aggregate
benchmark scores, which cannot tell whether an
agent executes a fixed plan well or strategically revises the plan when it fails. We separate these two capabilities: execution-level capability, iterating within an established training strategy, and strategy-level capability, revising that strategy as experimental evidence accumulates. Analyzing 1,338 post-training trajectories of frontier
agents, we find that
agents reliably execute post-training but lock into a default strategy, which follows the
agent rather than the task, and only 2.1% of transitions between adjacent training runs ever change strategy. We then test whether the
agent lacks experience,
reasoning, or the decision to switch. (1) Experience improves execution but not the strategy. (2) Additional
reasoning compute yields front-loaded gains on easier tasks but refines, rather than revises, the committed strategy. (3) Human review before training changes which strategy the
agent locks into, not whether it locks in, whereas a single mid-run instruction outperforms the
agent's own continuation by up to 17.44 points under the same budget. In conclusion, what the
agent lacks is the decision to reopen a committed strategy and try another one. Realizing RSI therefore calls for interaction protocols and training signals that make strategy revision an explicit, rewarded decision.
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All authors Joy Jia Yin Lim, Xin Huang, Hao Peng, Yaxi Lu, Xin Cong, Zhong Zhang, Maosong Sun, Yankai Lin
Artificial Intelligence · Cross-listed from cs.CV
GS-VLA: Plug-and-Play Viewpoint Canonicalization for Frozen VLA Policies via Gaussian Splatting
Yechan Park and HyunJin Kim
This paper proposes a lightweight, plug-and-play framework that improves robustness to viewpoint shifts in
Vision-
Language-Action (VLA) policies without policy retraining. To our knowledge, this is the first approach to directly leverage 3D Gaussian-based novel-view synthesis for observation-space adaptation in VLA policies. Current VLA performance relies on the implicit assumption that training and deployment camera configurations are identical. Our experiments show that even a small displacement of the camera mount can reduce the success rate on the LIBERO
benchmark from about 90% to about 10% in the worst case. Prior approaches, such as large-scale fine-tuning or generative data augmentation, are computationally expensive and risk catastrophic forgetting. To address this, viewpoint shifts are reformulated as a localized novel-view synthesis problem. Under a Locality assumption, that camera perturbations remain within a small bounded region relative to the workspace, viewpoint normalization reduces to a scene- and policy-independent disocclusion task. Our work implements this idea with a 4M-parameter 3D-Gaussian canonicalizer prepended to a frozen VLA policy. Without modifying policy weights, GS-VLA improves performance across three orthogonal axes: (1) Policy architectures, (2) Unseen task suites, and (3) Perturbation scales. These results show that a lightweight visual module can recover a large fraction of the performance lost under viewpoint shift, without policy retraining.
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Artificial Intelligence
Eureka: Task-Conditioned Meta-Agent Orchestration for Scientific Discovery
Alizer Wong, Heng Cui, Yi Tan et al. (+6 more)
We present Eureka, a task-conditioned Meta-
Agent architecture that compiles long-horizon tasks into dynamic obligation graphs with explicit acceptance semantics. During execution, Eureka forms Macro-
Agents with specialized state,
memory, operators, tools, verifiers, and local
topology via receding-horizon planning, architecture promotion, and minimal-sufficient compilation. When bottlenecks recur, cost-benefit-gated
evolution updates the local architecture under constraints. Theoretically, we establish results on regret, planning invalidation, amortization, subtree interfaces, serializability, and verification. Experimentally, Eureka completes 170/170 recursive tasks and generates 3,948 certificates with no false acceptances. Active context compresses median input from 9,490 to 4,005
tokens; incremental processing avoids 65.38% recomputation across 12,000 tasks; 16,000 concurrent executions serialize consistently. The same Meta-
Agent instantiates a Theory-Discovery
Agent and a Math/
Conjecture Agent. The former yields structural results in
quantum-process and spacetime theory. The latter identifies bottlenecks in Riemann Hypothesis research and advances a positivity certificate for Suzuki's localized Weil quadratic form to 0 < a <= 69/200 = 0.345, reaching ~99.55% of (log 2)/2. These results suggest that scientific-
agent capability depends not only on the base model but on whether an architecture can be formed to match the task's cognitive structure.
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All authors Alizer Wong, Heng Cui, Yi Tan, Xiongchao Zhan, Liang Lin, Yuxiang Guo, Zhaorong Dai, Zixin Zeng, Wenyuan Li
Artificial Intelligence · Cross-listed from quant-ph
Bernstein-Vazirani Networks: Quantum Machine Learning by Interference
Natacha Kuete Meli, Tolga Birdal, Prayag Tiwari et al. (+2 more)
We introduce Bernstein-Vazirani Networks (BVNs), a non-variational
quantum machine learning framework that leverages
quantum interference for supervised learning, demonstrated on
vision and representation learning tasks. In their standard form, BVNs follow the principle of
quantum Fourier sampling: labelled data are placed in superposition and interfered in the
Fourier basis to extract globally informative features. We then define generalised BVNs that enable interference in problem-adapted bases, yielding more expressive models under the same measurement budget as in the standard setting. BVNs achieve universal function approximation through (over)complete interference bases, while training of BVNs is gradient-free. Experiments on synthetic and real-world classification tasks, as well as implicit image representation, show strong generalisation capabilities and competitive performance with classical and
quantum baselines.
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All authors Natacha Kuete Meli, Tolga Birdal, Prayag Tiwari, Vladislav Golyanik, Michael Moeller