Artificial Intelligence · Cross-listed from cs.LG
SW-KAN: Kolmogorov-Arnold Networks with Stieltjes-Wigert q-Orthogonal Polynomials
Amirhosein Azarpour and Seyyed Moein Kazemi
Kolmogorov-Arnold Networks (KANs) represent a paradigmatic shift in deep learning by replacing fixed node activations with learnable univariate functions on edges, offering enhanced interpretability and parameter efficiency. While recent
polynomial-based KAN variants have addressed the computational overhead of original B-spline implementations, they introduce a fundamental yet underexplored challenge: the domain mismatch between unbounded real-valued inputs and the bounded or semi-infinite support of orthogonal
polynomial bases. To address this limitation, we propose the Stieltjes-Wigert Kolmogorov-Arnold Network (SW-KAN), a novel architecture that employs Stieltjes-Wigert q-orthogonal
polynomials defined on the semi-infinite domain (0, infinity). We introduce a smooth exponential-of-tanh mapping that stably bridges the domain gap while preserving well-conditioned gradients, and leverage a numerically stable three-term recurrence that evaluates
polynomial expansions in O(N) operations without special-function calls. Through comprehensive experiments spanning image classification and continuous function approximation, we demonstrate that SW-KAN achieves superior accuracy-efficiency trade-offs across diverse tasks. The log-normal weight structure and learnable q-parameter of Stieltjes-Wigert
polynomials provide a distinct inductive bias that enables robust performance under resource-constrained conditions, including reduced feature dimensionality and limited training data. The proposed architecture not only outperforms established
polynomial KAN baselines on standard
benchmarks but also exhibits strong representational capacity for approximating complex multivariate functions with remarkably few parameters, making it a compelling alternative for efficient function approximation and classification in resource-constrained settings.
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Artificial Intelligence
Characterizing a Configuration Where Inference-Time PRM-Pruned Fragment Grafting Is Inert: Evidence from Three Reasoning LMs
Khawaja Murad ul Hassan and Mehran Ebrahimi
Diversity collapse in parallel chain-of-thought has motivated inference-time interventions built on a natural design: when a process reward model (PRM) prunes a chain, its high-PRM prefix is extracted and grafted verbatim as an in-context demonstration into a still-decoding sibling. We isolate this mechanism, PRM-Pruned Fragment Grafting (PPFG), as the most cost-minimal operationalization of cross-trajectory step-level transfer, and test it at the operating point where prior fragment-grafting work reports gains only under additional compensating ingredients. On Qwen2.5-7B-Instruct with Math-Shepherd on full MATH500 (n=500, three seeds), PPFG in both stagnation- and random-targeting variants is statistically indistinguishable from an independent parallel-CoT baseline on every measured axis. We characterize why: a four-bucket classification of 322 stagnation-rule injection events shows only 14% targeted a genuinely struggling chain; the rest landed on chains that had already succeeded, were near completion, or sat on a flat PRM plateau, states a rescue graft cannot change. No compound-gate refinement jointly achieves well-targeted firing and adequate density, and a random control matches the same parity at 2.4x the firing rate, so the inertness is not heuristic-specific. The finding replicates across three base LMs, six
benchmarks, a second PRM, and a compatibility-gate sweep; two-one-sided-tests analysis promotes the parity to positive equivalence on all twelve Qwen/LLaMA
cells. A per-event spot-check finds injected chains prune at 2.75x the matched-step rate, but a surviving-sibling counterfactual finds no population-level compensation. A hindsight oracle bounds any per-problem gain from choosing PPFG over independent at +0.13 pp. We contribute an equivalence-testing template for establishing inference-time mechanism nulls, with every claim scoped to its tested operating point.
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Artificial Intelligence · Cross-listed from cs.MA
The Delegation Danger Band: Why Mid-Capability Sub-Agents Over-Trust Inherited Stale State
Jundong Hu and Shekar Ramachandran
Agent frameworks increasingly delegate work by forking sub-
agents; a common default makes the child inherit the parent's full working context. We measure how the effect of inherited state changes with capability, where $C_m$ denotes clean fork-fresh accuracy. We compare 3 inheritance policies: Reset (fork fresh: base evidence only), Selective (curated handoff: + the useful prior conclusion), and Full (implicit fork: + the useful conclusion and $d$ copies of a superseded conclusion) over a same-family ladder (Qwen3 0.6/1.7/4/8B) on a frozen, closed-set, action-scored
benchmark. Every task is solvable from the base evidence, so performance loss can be attributed to reliance on stale state. (1) Deference to superseded state falls sharply with measured capability $C_m$ (the slope's confidence interval, CI, excludes zero on every family) across 2 synthetic primitives plus MuSiQue and HotpotQA. (2) On the Qwen3 synthetic ladder, net inheritance harm follows a nonmonotone pattern: a mid-capability model (Qwen3-1.7B) is a statistically significant local minimum of net harm, falling below its fork-fresh baseline ($\Delta(32)=-0.19$ [-0.25, -0.12]) and both neighbors, while the weakest model stays near-neutral and the strongest models stay robust. We call this harmful capability range a danger band. A within-model counting-difficulty sweep shows that the effect depends on model class even at matched $C_m$, and a live parent-to-child fork reproduces the mid-model harm. (3) Curated Selective handoff improves average accuracy over Full on all 3
datasets, largest at the in-band model, while the fixed-threshold capability router fails on the other
datasets; a transferable router would need to predict the balance between reuse benefit and stale-context penalty. The
benchmark is frozen and version-hashed.
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Artificial Intelligence · Cross-listed from cs.LG
Not All Ranks Are Equal: Budget-Aware LoRA Merging Across Tasks
Avinash Amballa, Yashas Malur Saidutta, Wenbo Li et al. (+2 more)
Merging low-rank adapters (LoRAs) promises to eliminate the overhead of swapping task-specific weights at inference time. However, existing merging methods assume every layer needs the same rank budget. Further, some methods assume that rank budget needs to be split equally among the tasks too. We show this uniform-budget assumption is a major source of the performance gap between merged and per-task LoRAs. However, rank selection is an NP hard problem. To this end, we introduce Net Utility, a data free metric that first decomposes every task LoRA by its Singular Value Decomposition (SVD) and scores each of those singular directions by its task utility and its interference with other tasks directions. Next, we globally pool these scores to select singular directions with the highest values with a constraint on the total number of directions selected. The proposed Net Utility metric is applied on top of five different merging methods across three different merging spaces. The merging is done over two sets of tasks,
vision and
language tasks. Net utility based rank allocation outperforms its counterparts without that allocation. On average, over
vision tasks it achieves +2.1% improvement in performance, and +2.2% improvement over the
language tasks.
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All authors Avinash Amballa, Yashas Malur Saidutta, Wenbo Li, Lazar Valkov, Srinivas Chappidi
Artificial Intelligence
EdgeMem: LLM-Free Agent Memory Construction and Retrieval via Evidence-Preserving Multi-Anchor Hypergraph
Zeyang Cui, Jiannong Cao, Zhiyuan Wen et al. (+3 more)
Agent memory allows LLM
agents to use earlier interactions when answering new queries. Existing methods often compress interaction histories into summaries or other LLM-generated representations. Repeated generation adds cost and can discard answer-bearing details before the system knows what a future query will require. We propose EdgeMem, an
agent-
memory method built around a simple principle: preserve original interaction turns and organize them through complementary content, temporal, and episodic cues. EdgeMem realizes this principle with a multi-anchor hypergraph constructed by lightweight local processing.
Retrieval directly returns source evidence and reserves LLM use for final answer generation, combining structured access to multi-session histories with faithful retention of the original conversation. Experiments on LoCoMo and LongMemEval-S show strong
retrieval and
memory-grounded question answering; on LoCoMo, EdgeMem achieves the highest strict-judge score among seven reproduced systems under a shared
prompt (61.01 versus 58.70), while construction and
retrieval require no generative-LLM calls. Overall, EdgeMem shows that preserving and organizing source evidence provides an effective and efficient foundation for
agent memory without generative
memory management.
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All authors Zeyang Cui, Jiannong Cao, Zhiyuan Wen, Bo Yuan, Junlan Feng, Shengyuan Chen
Artificial Intelligence · Cross-listed from cs.HC
An Agent Model Abstraction for Human-AI Teaming Cognitive Coupling
Kolitha Kottagaha W.M, Jos A.C. Bokhorst, Ben Gaffinet et al. (+1 more)
Industrial environments increasingly rely on collaboration between humans and AI-enabled
agents. Effective teamwork requires aligning how
agents perceive situations, plan actions to pursue goals, and adapt to changing conditions, yet existing systems lack mechanisms for cross-
agent cognitive processes coupling. This paper presents a conceptual cognitive
agent model that formalises cognitive coupling through eight components: Input, Process, Output, State, Value,
Memory, World Model, and Goal. The model abstracts how
agents coordinate and co-regulate their cognitive cycles, providing a basis for analysing distributed cognition and designing cognitively interoperable human-AI systems.
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All authors Kolitha Kottagaha W.M, Jos A.C. Bokhorst, Ben Gaffinet, Christos Emmanouilidis
Artificial Intelligence · Cross-listed from cs.CV
Subject-Relative Micro-Motion and Sleep Dynamics for Near-Infrared Video Sleep Staging
Kunmin Jang, You Rim Choi, Hun Heo et al. (+5 more)
Near-infrared (NIR)
video is a promising modality for contactless sleep monitoring, but recent
video-based sleep staging methods often use it as a route to reconstructed respiratory/cardiac proxies or cross-modal physiological representations. We study
video-only sleep staging under labels defined by polysomnography (PSG), where the model infers sleep stages from NIR
video alone without explicit physiological proxy reconstruction or auxiliary physiological signal supervision. This tests whether NIR
video itself can provide informative sleep-stage evidence, rather than only serving as an input for recovering physiological proxies. We propose ViNUSS (
Video-Native Unmediated Sleep Staging), a framework that combines subject-relative micro-motion learning with full-night sleep dynamics modeling. Spatially anchored pre-spatial micro-motion encoding preserves localized temporal variation together with its spatial context. Within-subject stage contrast learns stage cues with respect to each subject's night-specific baseline. Two-scale sleep dynamics modeling captures within-epoch motion
evolution and organizes epoch-level evidence into a coherent full-night sleep-stage trajectory. On 475 overnight NIR recordings (~3,250 hours), ViNUSS achieves 0.80 accuracy and 0.78 macro-F1 for four-class sleep staging. Interpretability analysis suggests
attention to thoraco-abdominal periodic motion and gross body movements associated with arousals and position changes. These results support NIR
video as an independently informative and complementary modality for PSG-defined sleep-stage estimation
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All authors Kunmin Jang, You Rim Choi, Hun Heo, Heonjun Lee, Suahn Bae, Dongik Park, Hyun-Woo Shin, Hyung-Sin Kim
Artificial Intelligence · Cross-listed from cs.SE
Dynamic Adaptation of the LLM Context for Generating Routines with Coupled Semantics
Gnaneswar Villuri, Hashmath Shaik and Alex Doboli
LLM-based code generation fails when correctness depends on execution-dependent coupling: the meaning of one routine is defined by the runtime behavior of another, a relationship that cannot be resolved from textual descriptions alone. This limitation, which we call static binding, is not confined to explicitly coupled problems; it appears to varying degrees whenever correctness depends on joint execution behavior across components, from explicit cross-coupled optimizers to subtler joint constraints in packing, routing, and symbolic search. This paper proposes dynamic context adaptation, a sample-efficient validation-generation loop designed for this setting. A validation
agent extracts structured diagnostic information from execution traces, providing gradient-like guidance to a generation
agent that proposes multiple candidates per iteration. A knowledge graph derived from the problem description supplies semantic constraints to the generation
agent. Simulated annealing selects among candidates to avoid greedy collapse. Our method outperforms zero-shot, Reflexion, and OpenEvolve on seven of eight problems at both 300 and 600 evaluations (p < 0.01), a regime where population-based search has not yet accumulated sufficient diversity to compete. Notably, on the primary motivating problem (cross-coupled optimization), our method also achieves the best score at 1000 evaluations, consistent with the hypothesis that structured execution feedback is most beneficial when correctness depends on runtime coupling. Ablation results confirm that structured execution feedback is the primary driver.
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Artificial Intelligence
Extremely Sparse Supervision Incentivizes Reasoning Ability
Zhishuai Liu, Xingzi Xu, Mehmet Saygin Seyfioglu et al. (+2 more)
Large
language models demonstrate increasingly strong
reasoning capabilities through effective post-training. Yet, prevailing post-training methods optimize over massive numbers of
tokens, implicitly assuming that effective learning must be
token-intensive. We revisit this assumption in the on-policy
distillation (OPD) setting, which naturally admits dense teacher supervision at every generated
token. Using the Qwen3 family, we discover a counter-intuitive phenomenon:
reasoning can be effectively incentivized by an extremely small fraction of generated
tokens–as few as one or two
tokens per
reasoning trajectory, corresponding to only 0.05% of all
tokens. Surprisingly, this sparse supervision in most cases matches or surpasses full-
token training in improving
reasoning ability, despite excluding the vast majority of generated
tokens from the training objective. This phenomenon is consistently observed across nine teacher–student configurations spanning different model scales on mathematical
reasoning tasks, and is further validated on coding
reasoning, Llama models and Proximal Policy Optimization (PPO)-based reinforcement learning with verifiable reward (RLVR). Interestingly, such extremely sparse supervision may be closer to the natural learning process: rather than correcting every step word by word, one reflects on a few critical
reasoning steps, updates prior understanding, and continues the trial-and-error, avoiding micro-level corrections while remaining remarkably effective. Overall, our results challenge the assumption that effective post-training must be
token-intensive and point to a new direction for understanding and designing more efficient post-training
algorithms.
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All authors Zhishuai Liu, Xingzi Xu, Mehmet Saygin Seyfioglu, Pan Xu, Karim Bouyarmane
Artificial Intelligence
La Agente Óptima: Towards Agentic Self-Driving Laboratories
Marcel Müller, Jiaru Bai, Willi Gottstein et al. (+14 more)
Self-driving laboratories (SDLs) combine automated experimentation with adaptive decision-making to accelerate scientific discovery. Their operation nevertheless often depends on human specialists who translate scientific objectives into executable closed-loop campaigns. Specialists adjust them as data and operating conditions change. Here, we present La Agente Óptima, an agentic framework that constructs and supervises
Bayesian optimization campaigns across computational and experimental systems while maintaining a persistent optimization state. By separating large
language model (LLM)
reasoning from executed campaigns, Óptima runs repetitive optimization loops consistently, returns control to the
agent only when progress requires interpretation or campaign revision, and keeps every decision auditable. We evaluate Óptima across ablation studies, five digital discovery tasks, and two physical platforms. Throughout, Óptima maintained executable campaigns as both the scientific problem and execution environment evolved. In a closed-loop contact angle optimization campaign, Óptima identified and corrected a mid-run measurement failure, bringing the contact angle from 71.4 to 67.8 degrees, just above the 64-66 degree range. From this result, Óptima correctly inferred that the target was likely unattainable with the available reagents and recommended changing the formulation. In a five-day multi-objective flow-chemistry campaign, Óptima increased the yield from 30% to 59% over 23 experiments. Despite substantial inference costs, it cost less and used substantially less starting material than a human-directed campaign, while selecting a more mass-efficient operating point. These results show that LLM-based
agents can make rigorous, long-running optimization campaigns accessible to domain scientists without specialist setup, expanding the scope of SDLs.
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All authors Marcel Müller, Jiaru Bai, Willi Gottstein, Abhijoy Mandal, Mohammad Nazeri, Elia Savino, Yanlin Fang, Sujoy Das, Sergio Pablo García Carrillo, Yeonghun Kang, Juan B. Pérez-Sánchez, Simone Pilon, Martin Fitzner, Timothy Noël, Frank Gu, Varinia Bernales, Alán Aspuru-Guzik
Artificial Intelligence
Reducing Hallucinated Transcripts in Whisper via Hallucination Space Projection
Maryam Abbasihafshejani and Murtuza Jadliwala
Whisper is a widely used foundation model for automatic
speech recognition (ASR), but its generative decoder can produce fluent hallucinated transcripts for inputs containing little or no
speech. We propose a training-free, inference-time method to reduce these
hallucinations using low-rank projection of decoder activations. A compact
hallucination-associated subspace is estimated from non-
speech calibration data, and decoder hidden states are projected away from this subspace during inference. We evaluate two variants: always-on, which applies projection to all inputs, and gated, which applies it only when Whisper predicts that an input is likely non-
speech. Across non-
speech benchmarks, always-on projection reduces average
hallucination rate (HR) from 31.31% to 2.44%, a 92.21% relative reduction, while gated projection reduces HR to 3.74%, an 88.05% relative reduction, with lower false rejection of genuine
speech. On LibriSpeech, gated projection increases absolute word error rate (WER) by 0.33-4.39 percentage points and yields false-rejection rates (FRR) of 0.41–9.97% across model and split settings. These results show that low-rank activation projection can substantially suppress Whisper
hallucinations without retraining, while providing a controllable trade-off between
hallucination suppression and
speech recognition performance.
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Artificial Intelligence
IPGeoAI: Transformer-Based Geolocation with LLM Semantic Fusion
Avinash Kadimisetty, Andy Jinqing Yu, Philip Favaloro et al. (+2 more)
Accurate city-level IP Geolocation is an important enabler for the modern digital ecosystem, underpinning services ranging from local content delivery and targeting to digital rights enforcement. However, traditional heuristic and database-driven methods often struggle to resolve the complex, non-linear allocation patterns of modern network infrastructures, particularly within the exploding IPv6 address space and transient mobile networks. In this paper, we introduce IPGeoAI, a novel deep learning model architecture that reframes geolocation from a static lookup problem to a sequential modeling task. Our approach utilizes the
Transformer Encoder to capture hierarchical dependencies inherent in IP subnet structures. We propose a method to resolve geographic ambiguity by integrating unstructured semantic context via a Zero-Shot LLM Feature Extraction pipeline. We utilize Large
Language Models to transform raw, noisy Autonomous Systems (AS) descriptions into structured, domain-specific metadata (such as 'University' vs. 'ISP' or 'Global' vs. 'Local') via an offline pre-computation process. By fusing these semantic signals into the network via a Multi-Head Cross-
Attention module, we bridge the gap between numerical network
topology and real-world semantic identity. Extensive offline evaluation on a proprietary
dataset spanning 200,000 cities demonstrates that IPGeoAI significantly outperforms a leading external vendor in city-level granularity. By adopting a hierarchical inference strategy that refines coarse-grained country signals, our model achieves a 6% improvement in city-level accuracy while extending coverage to 100% of the traffic. Furthermore, in large-scale online production tests, the model drove a statistically significant +0.35% improvement in our 1st-tier downstream use cases metric.
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All authors Avinash Kadimisetty, Andy Jinqing Yu, Philip Favaloro, Wenlong Liu, Xiaolu Xiong
Artificial Intelligence · Cross-listed from cs.RO
Continual Field-Adaptive Models (CFAMs) for Post-Deployment Physical AI
Amarjot Singh, Tanmay R. Pancholi, Jainam Kothari et al. (+7 more)
Unattended interactive autonomy - machines that step into danger in place of humans and complete tasks with human tools - remains a missing capability in mission-critical operations. These domains offer scarce training data and only onboard compute, yet deployed systems must face novelty without erasing prior competence. We introduce Continual Field-Adaptive Models (CFAMs), which learn efficiently in the lab and continue learning after deployment through autonomous, gradient-free, on-device updates. CFAM uses a complementary learning architecture with a frozen slow-learning component and a fast-learning Capsule Field. The slow component contains three cortices: Sensor, which maps
multimodal input into 3D-grounded geometry;
Reasoning, which decomposes tasks into skills and evaluates outcomes; and Action, which executes
geometric skills. The Capsule Field stores field learning one-shot and gradient-free as Competence Capsules. Skill installation is few-shot in the lab and continual in the field; open-world novelty is outside scope. We evaluate CFAM across five embodiments: manipulator, quadruped,
humanoid, quadrotor, and off-road vehicle. Baselines (pi0, CogACT, SpatialVLA) use the same in-house multi-embodiment
dataset for physical-platform comparisons. CFAM reaches the operating point of a standard policy trained on the full prior-training
dataset using 40% of the data, or 2.5x fewer trajectories. At test time, autonomous capture of verified near-OOD cases improves action success by 13.9 percentage points. In sequential simulation, backward transfer is -0.5 percentage points versus -11.4 for LoRA. CFAM therefore provides a bounded form of post-deployment physical intelligence: few-shot skill learning, autonomous field growth from verified near-OOD experience, and retention of prior competence.
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All authors Amarjot Singh, Tanmay R. Pancholi, Jainam Kothari, Shrirang Mahajan, Ketan Bansal, Zackory Erickson, Giuseppe Loianno, Alexandre M. Bayen, Jeff Schneider, Vince Nakayama
Artificial Intelligence
From Answers to Interpretations: Rethinking Ambiguity-Induced Aleatoric Uncertainty Estimation in LLMs
Omer Nahum, Niv Nayman, Jonathan Fhima et al. (+4 more)
A key challenge in reliable LLM deployment is recognizing when uncertainty reflects irreducible variability in the task rather than limitations in the model's knowledge. In
language tasks, a central source of such aleatoric uncertainty is input ambiguity or underspecification, where multiple interpretations remain plausible. Existing decomposition methods estimate aleatoric uncertainty by generating multiple clarifications of the input, querying the model for an answer under each clarification, and comparing the resulting answers. We argue that answers are not necessary for identifying ambiguity: they are often redundant, add avoidable cost, and can mislead through epistemic leakage. We support this claim theoretically, and propose a clarification-only approach that estimates this ambiguity-induced component directly from the space of plausible interpretations, without answers to the clarified inputs. Using ambiguity detection as an operational evaluation across three
benchmarks, this direct approach improves AUROC (63.34 vs. 60.85), reduces computational cost by 4-26x in output
tokens and 2.2-3.5x in API calls, and yields estimates with substantially lower correlation with epistemic uncertainty. Overall, our results suggest that ambiguity-induced aleatoric uncertainty is better estimated from the interpretation space than from the response space.
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All authors Omer Nahum, Niv Nayman, Jonathan Fhima, Alon Zolfi, Jeremy Levy, Shai Mazor, Paolo Favaro
Artificial Intelligence
Data-Driven Discovery of Composition-Dependent Constitutive Models for Hyperelasticity and Viscoelasticity of Digital Materials
Josué García-Ávila, Beijun Shen, Manuel K. Rausch et al. (+2 more)
Digital materials fabricated by multi-material 3D printing are designed as controlled mixtures of stiff and compliant constituents, yielding effective responses that span more than an order of magnitude in apparent stiffness and exhibit strongly nonlinear, composition-dependent, and rate-dependent dissipative behavior. Classical finite-strain viscoelastic models represent such behavior with closed-form strain energy functions for
equilibrium and non-
equilibrium stresses as well as
evolution of internal variables, which may limit flexibility when a single constitutive model is expected to generalize across materials and loading rates. Here, we present a data-driven multi-material constitutive modeling framework that generalizes a formulation by Bergström and Boyce. The proposed framework retains the structure of the classical model, namely multiplicative kinematics, invariant-based strain-energy functions, and a scalar dissipative
evolution law directed along the normalized nonequilibrium deviatoric stress. For the
equilibrium branch, the data-driven discovery framework either directly predicts closed-form model parameters as functions of composition or automatically constructs a polyconvex strain-energy function using neural ordinary differential equations (NODEs). The nonequilibrium branch kinetics are learned similarly, either by directly identifying closed-form parameters across compositions or by using appropriately constrained artificial neural networks. Using multi-rate uniaxial compression data across multiple material compositions, we show that the proposed formulation captures rate-dependent stiffness and hysteresis across compositions while preserving thermodynamic consistency.
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All authors Josué García-Ávila, Beijun Shen, Manuel K. Rausch, Mary C. Boyce, Adrián Buganza-Tepole
Artificial Intelligence · Cross-listed from cs.CR
Repeat-After-Me: Black-Box Adaptive Visual Prompt Injection
Sizhe Chen, Yu-Lin Tsai, Ivan Evtimov et al. (+4 more)
Prompt injection is widely recognized as a major security threat to AI
agents that interact with untrusted external data, such as websites, documents, and emails. Prior work has shown that, in the text domain, black-box
prompt injection can achieve near-perfect attack success rates (ASRs). In the image domain, however, existing visual
prompt injection methods are substantially less effective in attacking frontier commercial VLMs for materially harmful behavior. Achieving such outputs is hard because it requires a long and/or format-compliant target string, such as a precise, parseable native tool call with exact function names and arguments. We present Repeat-After-Me, a black-box adaptive visual
prompt injection attack that can reveal personally identifiable information or make malicious tool calls. Across both open-weight and commercial frontier VLMs, including Qwen3.6-27B and GPT-5.5, our method achieves ASRs exceeding 82% and 47%, respectively, under a realistic setting in which the benign user
prompt is semantically unrelated to the injected task and does not verbally authorize it. Our optimized injection has non-trivial attack transferability across commercial VLMs and benign samples. We show our attack works in cases where adaptive textual
prompt injection fails. In a real-world OpenClaw
agent connected to Discord, an untrusted user can use a minimally injected image from our attack to overwrite TOOLS.md, enabling future sensitive behaviors like remote code execution and secret exfiltration. We discuss potential defenses.
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All authors Sizhe Chen, Yu-Lin Tsai, Ivan Evtimov, Kamalika Chaudhuri, Raluca Ada Popa, David Wagner, Arman Zharmagambetov
Artificial Intelligence
Towards a universal language of concepts: A survey
Aishni Parab
Humans can learn and generalize novel concepts from sparse data because they express knowledge in rich structural formats. In this paper, we propose that programs are a strong candidate for universal representation of concepts. We review computational models of concept learning that use programs as their concept representation and evaluate their contribution toward a universal representational
language.
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Artificial Intelligence
MaxKernel: Agentic Kernel Generation for TPUs
Shangkun Wang, Nina Cai, Charles Hoong et al. (+7 more)
Designing and authoring high-performance custom kernels for accelerators is a complex task that requires deep hardware-level expertise. Large
Language Models (LLM) can be leveraged together with real-time compiler feedback to build agentic systems for kernel generation. In this work, we present MaxKernel, a multi-
agent system that implements three distinct paradigms for TPU kernel development: (1) a Human-in-the-Loop (HITL)
agent for collaborative, step-by-step design; (2) an Autonomous (Auto)
agent that executes a fully automated, metric/trace-driven optimization loop; and (3) a Graph-Based Autonomous Search that scales the Auto
agent for global exploration of the design space. All three paradigms leverage a shared pool of specialized sub-
agents to handle planning, implementation, self-debugging, testing, and hardware profiling. We evaluate MaxKernel on JaxBench, a comprehensive suite of 50 diverse kernel tasks for TPUs, alongside complex, real-world workloads from state-of-the-art open-source models. We demonstrate that MaxKernel consistently generates highly optimized implementations, matching expert hand-tuned baselines and delivering significant performance across the
benchmark. Our
agent is open-sourced and available https://github.com/AI-Hypercomputer/accelerator-
agents/tree/main/MaxKernel.
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All authors Shangkun Wang, Nina Cai, Charles Hoong, Julian Walker, Gerson Kroiz, George Vanica, Deepak Patil, Andi Gavrilescu, Hassan Sipra, Sethu Sankaran
Artificial Intelligence
Multi-Harness Reinforcement Learning for Coding Agents: Training Gains and Harness Dependence
Chenqian Le, Jiayi Cheng, Qijia He et al. (+3 more)
Recent systems train coding
agents with reinforcement learning (RL) on trajectories collected through multiple execution harnesses. However, gains under the training harnesses leave open whether the improvements transfer to unseen interfaces and how they depend on reward normalization. We present a controlled study that measures RL gains under fixed harnesses and compares the same models across harnesses. We train two Qwen3-8B RL variants from the same supervised fine-tuning (SFT) checkpoint using identical replay data. They differ only in whether rewards for each task are normalized within each harness (Within) or across harnesses (Cross). On SWE-bench Verified, we identify three main findings: (i) Multi-harness RL yields modest gains over the SFT baseline under both normalization rules. Across the four training harnesses, the estimated mean improvements are +0.48 and +0.77 percentage points for Within and Cross, respectively. (ii) Cross increases the fraction of trajectories with nonzero advantage but provides no consistent advantage over Within on an unseen harness across training seeds and evaluation budgets; neither rule significantly improves over SFT on this harness. (iii) Performance varies substantially more across harnesses than with RL training. After averaging over model variants, the highest and lowest source-harness solve rates differ by 7.13 percentage points, far exceeding the estimated training gains. Even after multi-harness RL, the observed performance differences across execution interfaces remain substantially larger than either the gains from training or the differences between normalization rules.
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All authors Chenqian Le, Jiayi Cheng, Qijia He, Runhao Li, Yinghao Li, Xupeng Chen
Artificial Intelligence · Cross-listed from cs.SD
Pitch-class Steering for Diffusion-based Music Generation via Latent-space Probes
Yushi Ye, Wilson Zheng and Yongyi Zang
Recent work on controllable music generation has focused on autoregressive models, leaving
diffusion-based systems comparatively underexplored. We present a lightweight method for steering the pitch content of audio produced by Stable Audio Open, a latent
diffusion model for music synthesis. A small convolutional probe containing approximately 125k parameters is trained to decode frame-level pitch-class activations from the model's variational autoencoder latent space, using paired audio and MIDI data. At inference time, the frozen probe serves as a differentiable loss function: its gradient with respect to the denoising latent is used to nudge generation toward a user-specified pitch-class sequence, requiring no retraining or architectural modification of the base model. Across 27 evaluation trials spanning 9 text
prompts and 3 target melodies, probe-guided generation increases melodic coherence by 2.4x over the unguided baseline (p < 1e-5, Wilcoxon signed-rank test), demonstrating that musically meaningful structure is both recoverable and steerable in
diffusion-based music latent spaces.
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Artificial Intelligence · Cross-listed from cs.DC
Atlas: Optimizing Deployment of Compound AI Workflows on Heterogeneous Clusters
Milos Gravara, Andrija Stanisic and Stefan Nastic
Compound AI workflows are increasingly used to serve complex AI tasks by coordinating multiple AI models and software components. This approach enables deployment flexibility, as each workflow stage can expose different model variants and resource requirements, but it also expands the deployment choices. A deployment must choose an execution plan that selects AI models for each compound AI workflow stage and places them on a heterogeneous cluster in order to satisfy SLOs. Deployment optimizers therefore need estimates to compare many candidate plans and identify feasible ones. System metrics can often be profiled per stage and composed according to workflow
topology, but accuracy cannot, as errors and information loss at upstream stages affect the accuracy of downstream stages. Existing approaches either profile complete configurations end to end, which scales poorly, or use product-based accuracy surrogates that treat stages as independent and can misrank candidate plans. We introduce Atlas, a framework for optimizing compound AI deployments under SLO constraints. Atlas uses MAP, a Markovian Accuracy Predictor, to estimate configuration accuracy from local conditional accuracy transitions between adjacent workflow stages. MAP discretizes intermediate outputs into accuracy buckets and composes transition profiles according to workflow
topology, giving the optimizer an accuracy estimate without exhaustive end-to-end profiling. Atlas formulates execution-plan selection as a mixed-integer linear program that maximizes predicted accuracy subject to SLOs. Across four compound AI workflows, MAP achieves Spearman correlation up to 0.947 while reducing profiling cost by up to 2.6x relative to exhaustive end-to-end profiling. Guided by MAP, the Atlas optimizer selects execution plans within 0.03 of oracle accuracy while reducing deployment cost by up to 42% through heterogeneous placement.
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Artificial Intelligence · Cross-listed from cs.DB
A Semantic Model of Genetic Evidence: A Step Toward Bridging the Basic-Science-Clinic Gap
Michael Bouzinier and Dmitry Etin
Scientific and clinical decision-making depends on evidence from the primary literature, but existing standards for representing that evidence (FHIR Evidence, ECO, SEPIO, and the GA4GH Genomic Knowledge Standards) are oriented toward clinical-trial workflows, evidence codes, or single-variant assertions, and do not capture the fine-grained, domain-specific structure of claims in basic and pre-clinical research. We introduce a semantic model for scientific evidence with three core classes, specialize it for genetics, align it structurally to FHIR Evidence with a SEPIO-anchored credibility decomposition, and attach a compact dimensional vocabulary whose conditional-activation rules are validated by a SHACL schema for the implemented constraints. Using clinical variant interpretation as the driving use case, we evaluate the model through a human-AI annotation pilot over six genetics papers, yielding 28 evidence items and 95 source-anchored assertions, with a workflow that keeps curator-authored reference annotations distinct from AI-drafted annotations. Treating the pilot as a feasibility study rather than a
benchmark, we argue that the model is a useful increment toward trustworthy, AI-ready infrastructure for variant interpretation: a reference data model and validation schema for representing genetic evidence.
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Artificial Intelligence
BioSync: Transformer-Based Cross-Modal Fusion for a Multimodal Physiological Digital Biomarker
Seyed Mahmoud Sajjadi Mohammadabadi
Cardiac, neural, behavioral, and
speech measurements from wearable and mobile devices provide partial, noise-sensitive views of physiological state. BioSync combines these measurements into the BioSync Index (BSI), a continuous composite digital biomarker defined under the BEST framework. The model applies multi-head self-
attention to modality
tokens and adds a linear branch whose hypothesis class includes standard feature concatenation. This architecture is motivated by latent-variable measurement theory and by the possibility that joint observations contain information unavailable from individual modalities. We evaluated BioSync on two literature-informed synthetic cohorts: a four-modality cognitive-decline cohort using HRV,
EEG, actigraphy, and
speech, and a metabolic-autonomic cohort structured around the public AI-READI wearable schema. In the cognitive cohort, BioSync and concatenation obtained AUCs of 0.928 and 0.926, respectively. In the metabolic cohort, BioSync obtained accuracy/F1 of 0.764/0.766, compared with 0.756/0.758 for concatenation. The BSI correlated with latent severity in both cohorts ($r=0.91$ and $r=0.68$). A pure-
attention ablation obtained cognitive-cohort AUC 0.911, locating the increase to 0.928 in the combined wide-and-deep architecture. With matched modality-dropout training, BioSync led concatenation at five of six cognitive-cohort corruption rates and at the highest metabolic-cohort rate. Its cognitive-cohort AUC was also higher than five published digital-biomarker reference values, although differences in
datasets and tasks preclude a controlled
benchmark claim. Comparison with single-modality, early-fusion, and late-fusion designs across six prespecified criteria identifies the model's computational properties; validation on real cohorts remains necessary.
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Artificial Intelligence
Rethinking Indirect Prompt Injection as a Test-Time Search Problem
Duong M. Nguyen, Joon Sik Kim, Blazej Manczak et al. (+1 more)
We formulate indirect
prompt injection as a test-time search over a task-dependent attack surface induced by the environment, user task, and injection task. To operationalize this formulation, we introduce an agentic attacker with a dedicated search harness that performs environment reconnaissance, structured
reasoning over attack strategies, and adaptive evaluation using victim-
agent feedback. Across heterogeneous tasks, we find that increasing attacker test-time compute improves vulnerability discovery and exploitation, while ablations show that explicit strategy management is important for avoiding redundant search and sustaining gains at larger budgets. These results suggest that agentic security evaluations should characterize both the attacker's search procedure and compute budget, rather than treating attack success as a budget-independent property of the victim. More broadly, our findings identify the attacker's adaptive search over the system attack surfaces as an important and underexplored security risk for tool-using
agents.
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All authors Duong M. Nguyen, Joon Sik Kim, Blazej Manczak, Vaikkunth Mugunthan
Artificial Intelligence · Cross-listed from cs.LG
Hakken: Predicting future discoveries to fill the gaps in today's knowledge
Tarek R. Besold, Uchenna Akujuobi, Pablo Sanchez et al. (+21 more)
We present Hakken, a domain-agnostic prediction and explanation system performing knowledge prediction, i.e., growing scientific knowledge by establishing novel relationships, ones that are not limited to the deductive hull of previous knowledge. Hakken uses a
transformer-based prediction model built on temporal sequences of knowledge graphs extracted from vast bodies of research publications, fused with an LLM's semantic knowledge, to predict the presence and define the type of as-yet undocumented relationships between scientific concepts. It then calls a model-agnostic explanation framework to provide accompanying information for each prediction that allows scientists to evaluate the suggested new relationship. While general purpose, we demonstrate Hakken's practical capabilities by applying it to the biomedical domain. There, Hakken's prediction model establishes a new
benchmark for time-aware multi-label relation prediction, and we show that the model's output stays coherent and informative over extended time spans in historic data. In addition, we scored 1.5 million above-confidence-threshold hypotheses related to aging, qualitatively validated batches of these predictions with biologists and progressed three of them for empirical validation in wet-lab. Two predictions with potentially significant impact in the context of drug discovery and repurposing were confirmed, introducing previously undocumented interactions between TP53 and BAMBI, and between RAF1 and TNF, to biomedical science.
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All authors Tarek R. Besold, Uchenna Akujuobi, Pablo Sanchez, Alessandra Toniato, Kana Maruyama, Jihun Choi, Samy Badreddine, Frederick Gifford, Daniel Evans-Yamamoto, Sucheendra K. Palaniappan, Miquel Ferrer, Kae Nagano, Iris Rossell, Tom Joy, Hatem ElShazly, Chrysa Iliopoulou, Christoph Wehner, Thiviyan Thanapalasingam, Susana Nunes, Pedro G. Cotovio, Peter Wurman, Peter Stone, Hiroaki Kitano, Michael Spranger
Artificial Intelligence
ResLearn-XR: Residual Learning for Network Traffic and Quality-of-Experience-Aware Modeling in Extended Reality
Yoga Suhas Kuruba Manjunath, Jie Gao and Lian Zhao
We present ResLearn-XR, a residual learning framework for predicting eXtended Reality (XR) network traffic and estimating Quality-of-Experience (QoE) risk. ResLearn-XR adopts a two-stage temporal learning structure comprising a base sequence prediction model augmented with task-specific residual learning components to improve adaptability to bursty, non-stationary XR traffic dynamics. The residual learning stages operate in the value space for continuous XR traffic
forecasting and in the logit space for probabilistic QoE risk estimation. \rev{For the QoE-risk branch, we introduce a Data Descriptor
Algorithm (DDA), a causal feature-construction module that converts packet-level application-layer observables into frame-timing-aware descriptors suitable for encrypted traffic analysis. We also construct an XR Traffic-QoE
dataset that pairs continuous XR traffic traces with session-level user-reported QoE labels. ResLearn-XR reduces SMAPE by up to 17.84% across frame-count, frame-size, and inter-arrival-time prediction, while reducing QoE-risk estimation SMAPE by up to 87.8% over single-stage baselines.
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Artificial Intelligence
When Quantization Breaks Memory: Recurrent-State Write-Back in Low-Precision Temporal Inference
Ismail Erbas, Xavier Intes and Vikas Pandey
Quantization is widely used to reduce the computational and
memory demands of neural-network inference. In recurrent networks, however, the quantized state is stored and returned at the next time step, so the rule used to store that state can alter subsequent computations. Here, we introduce recurrent-state write-back to denote this rule and isolate its effect in a compact GRU encoder–decoder for fluorescence lifetime imaging, a molecular imaging modality used in quantitative biological imaging. A central task is estimating two lifetime parameters, the short-lived component {\tau}1 and the long-lived component {\tau}2, from high-noise time-resolved fluorescence signals. Holding the trained model fixed, replacing continuous state propagation with deterministic 4-bit state storage increases estimation errors for {\tau}1 and {\tau}2 by approximately 70x and 300x, respectively. Failure occurs when repeated small updates remain below the write threshold, leaving the stored state nearly fixed while the network continues to propose change. Error feedback, residual
memory, and direction
memory carry information from these suppressed updates across time and recover accuracy without retraining. Precision sweeps show that increasing state precision can worsen a fixed recurrent solution, while matched training shows that compatibility with the state interface can be learned. To test whether this behavior extends beyond the GRU, we repeat the post-training intervention in an independently trained LSTM, where coarse write-back reproduces the failure, error feedback restores accuracy, and state-specific interventions reveal greater sensitivity of the
cell state than the hidden state. Our results establish recurrent-state write-back as a key determinant of low-precision recurrent dynamics and identify the state-storage interface as a central design consideration for quantized recurrent inference.
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Artificial Intelligence · Cross-listed from cs.CL
Towards Understanding Pause Token Fine-Tuning Dynamics: A Mode Retention Perspective
Jaehyeon Kim, Suhwan Kim, Nakyung Lee et al. (+4 more)
Pause-
token methods improve LLM
reasoning by inserting special
tokens into sequences. Prior work explains these gains through computational expressivity. However, there is relatively little investigation into the training dynamics of pause
tokens. We explore how pause
tokens reshape the training dynamics of fine-tuning. Two controlled pilots expose distinct asymmetries. On a synthetic continual-learning task, masked pauses overwrite a previously-learned distribution roughly 4x less at matched final adaptation (H1, mode retention); on a synthetic math-
reasoning probe, the boundary-adjacent
token comes to encode substantially more downstream-step information (H2, non-myopic compression). We formalize a training rule consistent with both - Masked Boundary Pause (MBP), pause
tokens placed at
reasoning-step boundaries with their loss masked. Across 1B-8B Qwen and Llama models, MBP consistently improves
reasoning, achieving gains of up to 6 points on math and 2.5 points on code, while preserving general
language understanding abilities. We further demonstrate that this mode-preserving strategy extend gains to GRPO. These results recast pause
tokens as a training-dynamics intervention on the retention-adaptation trade-off, rather than merely an inference-time computation device.
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All authors Jaehyeon Kim, Suhwan Kim, Nakyung Lee, Yeongoon Kim, Jimin Seo, Giho Lee, Jungwoo Lee
Artificial Intelligence · Cross-listed from cs.CL
Cultural Misalignment in Large Language Models: Detection, Measurement, and Mitigation Through Targeted Fine-Tuning
Antoni Czolgowski and Abel Iyasele
We evaluate three open-weight LLMs (Gemma3-12B from the USA, Bielik-11B-v3 from Poland, and Qwen3-4B from China) against World Values Survey Wave 7 data for 63 demographic personas across three countries, using normalized Wasserstein distance to quantify distributional misalignment. Contrary to expectations, no model favors its home country: the Chinese-built Qwen3-4B performs worst on its own Chinese population (W1 = 0.436, the highest misalignment in the entire model x country
matrix). Targeted LoRA fine-tuning on the five worst-case personas, requiring fewer than 1,200 training pairs and under 15 minutes on a single GPU, reduces bias by 16.8% for Bielik-11B (p_Bonf = 0.002, d = -4.4) with all five targets improving. However, country-level decomposition reveals that fine-tuning redistributes rather than removes bias: Bielik's worst-case personas swap entirely from American to Chinese elderly, with zero overlap between pre- and post-correction sets. To our knowledge, this is the first study to target worst-case demographic personas with LoRA fine-tuning for cross-cultural bias mitigation.
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Artificial Intelligence · Cross-listed from cs.CL
Patterns of Priming in Production: Lexical, Semantic and Structural Alignment in Language Model Generation
Giulia Pucci, Ruizhe Li and Arabella Sinclair
This paper investigates structural priming in
language model (LM) production, examining how preceding structural context influences sentence completion. While prior work has demonstrated priming effects in comprehension of structural alternations, it remained unclear whether these persist in production, where, when generating, an LM samples from many possible continuations at each step. We address this question through a series of controlled sentence-completion experiments on dative constructions. In line with prior work, we find that LMs are susceptible to structural priming, particularly in sentences that are semantically coherent. In terms of priming magnitude, we find that while there is a greater relative increase of double-object datives against our baselines, in line with inverse frequency effects, there is a larger absolute increase in prepositional-objects, the more frequently produced construction. Finally, we not only observe that structural priming is boosted by lexico-semantic coherence, but that structurally primed completions display greater levels of lexico-semantic repetition. Taken together, our evidence supports the view that structural priming in LMs operates across multiple levels of linguistic representation, facilitating, and facilitated by syntactic, lexical, and semantic
alignment. Code: https://github.com/the-context-lab/primedproduction.
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