Artificial Intelligence
Agent Memory with Episodic Retrieval for Financial Decision-Making
Nuoyue Xu, Jiang Liu, Wenxuan Huang et al. (+3 more)
Large
language models (LLMs) have demonstrated strong capabilities in financial analysis and
reasoning, inspiring recent advances in
agent-based trading frameworks. While these systems show promise, prior approaches either emphasize long-horizon
forecasting or operate as stateless analyzers, limiting their applicability to the demands of trading in complicated settings. To address these gaps, we introduce META (
Memory Enhanced Trading
Agent), the first RAG-like episodic-
memory-augmented multi-
agent framework for financial decision making. META integrates a family of specialized indicator
agents (e.g., Trend, MACD,
Stochastic, RSI, SMA, AVWAP, Heikin-Ashi) with a Decision
Agent that fuses their reports, and a
Memory module that retrieves and updates past trading episodes encoded as
market state
embeddings with outcomes and reflections. By recalling relevant experiences and adaptively reweighting signals under similar
market regimes, META achieves improved directional accuracy and robustness under short-horizon evaluation. Our results demonstrate that episodic
memory provides a powerful mechanism for regime-aware, interpretable, and low-latency decision-making in trading and decision making. The code of this project is released on GitHub.
Read the full abstract ▾
All authors Nuoyue Xu, Jiang Liu, Wenxuan Huang, Xiang Zhang, Juntai Cao, Jiaqi Wei
Artificial Intelligence
Reinforcement Learning with Verifiable Rewards for Small Search Agents
Gaurisankar Jayadas, Aske Plaat, Álvaro Serra-Gómez et al. (+1 more)
Reinforcement Learning with Verifiable Rewards (RLVR) performs well on problems with clear rewards, such as mathematics and coding, but whether it also works where the reward is less clear remains open. The reason-over-search recipe applies RLVR to open-domain question answering, where
retrieval grounds the answer and a match against the reference supplies the reward. So far it has been demonstrated on large models, and below one billion parameters only with
distillation from a larger teacher. We test the recipe on a small model. We train Qwen3.5-0.8B with Group Relative Policy Optimization (GRPO) and an interleaved Wikipedia-search tool on MuSiQue, varying only the reward across three shapes over three seeds each, and we evaluate every checkpoint held-out on a seven-
benchmark question-answering suite. The recipe works: the best run reaches 0.352 average exact match against a 0.092 untrained floor, a 3.8-fold gain, with no
distillation step in the training loop. The reward shape also matters. The Search-R1-faithful exact-match-only reward is the worst of the three at every seed at the matched training horizon, and it is worst even on exact match, the metric it directly optimises. We conclude that the sparse exact-match reward, RLVR's default in mathematics and code, is the wrong starting point for models of this size. The reason-over-search setting can supply a suitable reward for RLVR on small models, but small-model RLVR needs its own reward-design study rather than a scaled-down copy of a large-model recipe.
Read the full abstract ▾
All authors Gaurisankar Jayadas, Aske Plaat, Álvaro Serra-Gómez, Sandheep P
Artificial Intelligence · Cross-listed from cs.DB
KathDB-FAO: Synthesized Query Plans in a Multimodal DBMS
Guorui Xiao, Douglas Brown, Artur Borycki et al. (+1 more)
We design, implement, and evaluate KathDB-FAO, a new query evaluation subsystem for our KathDB
multimodal DBMS. KathDB-FAO takes as input a query in natural
language (NL) and converts it into a query execution plan where each operator is a function whose body is synthesized during query evaluation, which allows powerful query-specific optimizations. To generate accurate and efficient plans from NL, KathDB-FAO first extracts fine-grained atomic actions for correctness, then establishes contracts on the inputs and outputs of those actions and groups them for efficiency, and finally synthesizes the function for each group on the fly. On SemBench, KathDB-FAO cuts execution cost by 58.8% on average across scenarios compared with the next best system, at comparable or better quality.
Read the full abstract ▾
All authors Guorui Xiao, Douglas Brown, Artur Borycki, Magdalena Balazinska
Artificial Intelligence · Cross-listed from cs.CL
Technical Manual for Toolkit for Confidence-Corpus Consistency, Corpus Absorption and Rule Learning via Fine-Tuning on a Fabricated Corpus
José Luciano Verçosa Marques, Frederico Jorge Heitmann, Daniel Omar Perez et al. (+3 more)
This manual documents version 2.0.0 of an open toolkit for fine-tuning small causal
language models on fabricated and rule-governed arithmetic corpora and measuring what they take up from them. The fact domain is the 81 additions of two single-digit natural numbers, small enough to be enumerated exhaustively. The toolkit fine-tunes a model on the correct sums, on one fixed fabricated answer for every addition, and back on the correct sums of a subset of the additions; it fine-tunes copies of these models on simple rules (the sum plus a constant) and on a conditional rule (a shift that depends on the order of the addends), each paired with a control that has the same answers but no rule; and it measures every model on every candidate answer of every addition with one unchanged procedure, reporting results separately for additions seen in fine-tuning and additions held out. We describe and justify each stage of the pipeline: the confidence index (the probability of a complete answer, closed by an end marker), the single candidate set, the answer-only training loss, the lineage of fourteen measured models, the held-out split, the controls, the exclusion of additions that would count as hits by coincidence, and the exact and resampled intervals attached to every result. We then explain every figure and table a run produces and how each is read. This manuscript is a methodological and implementation reference: it documents the instrument, and it neither states nor tests hypotheses, nor reports or interprets the outcome of any specific run. Those are the subject of work that uses the toolkit. The toolkit and its pinned dependency environment are archived separately (Section 10) under a persistent identifier, to be cited as an instrument.
Read the full abstract ▾
All authors José Luciano Verçosa Marques, Frederico Jorge Heitmann, Daniel Omar Perez, Reinaldo Cesar, Marcelo Vinicius de Paula, Tárcio André dos Santos Barros
Artificial Intelligence · Cross-listed from cs.LG
Policy Complexity, Reaction Time, and Bounded Rationality in Reinforcement Learning
James Wu and Chris R. Sims
Biological
agents do not learn under conditions of unlimited computation. For humans, learning and choice are shaped by constraints on perception,
attention, and working
memory, which limit how much state information guides behavior and therefore bound policy complexity. Standard reinforcement learning models typically optimize reward without explicitly representing these internal costs, making them less suitable as models of biological intelligence. We derive MI-SARSA, an on-policy temporal-difference
algorithm that incorporates mutual-information regularization through a learned marginal action prior and a penalty on state-specific deviations from that prior. This yields a sequential learning model in which state information is used selectively when its expected return benefit justifies the added informational cost. Critically, the same state-specific information cost that governs policy compression also generates trial-level predictions for reaction time, distinguishing MI-SARSA from most reinforcement learning models, which predict choices or returns but not latency. Empirically, MI-SARSA produces a reward-complexity tradeoff, and stronger information penalties produce simpler policies with lower control costs and faster reaction times. Under environment shift, increasing regularization reduces post-switch performance degradation but also lowers asymptotic return, revealing a robustness-capacity tradeoff. Together, these results position MI-SARSA as a model of bounded sequential learning under cognitive constraints.
Read the full abstract ▾
Artificial Intelligence · Cross-listed from cs.RO
Temporal Learning for End-Effector Position Estimation under Aerodynamic Disturbances in Aerial Continuum Manipulation
Niloufar Amiri, Houman Masnavi and Farrokh Janabi-Sharifi
This paper investigates temporal neural networks for \mbox{end-effector} position \mbox{estimation} of an aerial continuum manipulator (ACM) operating under aerodynamic effects induced by the unmanned aerial vehicle (
UAV). An experimental
dataset is collected under stationary (\mbox{rotor-off}) and \mbox{free-hovering} conditions across continuum
robot (CR) configurations and
UAV altitudes, providing \mbox{end-effector} position measurements with and without aerodynamic residuals. To establish a nominal framework, \mbox{strain-parameterized} kinematic models with progressively richer strain bases are evaluated to balance model complexity and prediction accuracy. The selected nominal model then serves as the baseline for 3D position residual estimation using a \mbox{closed-form} \mbox{continuous-time} (CfC) neural network, with a multilayer perceptron (MLP) and a gated recurrent unit (GRU) used for comparison. On unseen test experiments, the CfC achieves an RMSE of \(22.00\pm1.70~\mathrm{mm}\) over five random seeds, compared with \(36.38\pm3.58~\mathrm{mm}\) for the MLP and \(27.72\pm2.92~\mathrm{mm}\) for the GRU, corresponding to reductions of \(39.52\%\) and \(20.62\%\), respectively. These results demonstrate the effectiveness of \mbox{continuous-time} learning for \mbox{end-effector} position estimation under aerodynamic disturbances relative to static and \mbox{discrete-time} learning methods.
Read the full abstract ▾
Artificial Intelligence · Cross-listed from cs.CL
An Explainable DistilBERT-BiLSTM-Attention Framework for Binary and Multi-Class Hate Speech Detection
Rameesha Zia and Muhammad Shahid Iqbal Malik
Hate
speech on social media poses serious risks to social harmony, mental well-being, and public safety, making its timely and accurate detection essential for content moderation systems. Most existing studies focus on binary classification, evaluated their frameworks on a single
dataset, and provide limited insight into how decisions are made, which limits their real-world applicability. In addition, limited work is done on the explainability of their predictive inference. To address these challenges, this study proposes a multilevel and explainable hate
speech detection framework. The proposed model integrates DistilBERT (Distilled Bidirectional Encoder Representations from
Transformers)
embeddings with a Bi-LSTM (Bidirectional Long Short-Term
Memory) model, and an
attention mechanism to capture both contextual meaning and sequential dependencies in text. To enhance trust and transparency, LIME (Local Interpretable Model-agnostic Explanations) is employed to explain model predictions by highlighting influential textual features. The framework is evaluated on two
benchmark datasets using both binary and multi-class classification to examine robustness and generalization. In addition, an ablation study is presented to highlight the significance of various components of proposed framework. For binary classification, the proposed model achieves F1-scores of 96.78% on the Davidson
dataset and 99.53% on the SMHS
dataset. In the multi-class setting, it attains F1-scores of 97.00% and 94.99% on the Davidson and SMHS
datasets, respectively, outperforming existing baseline approaches. The results demonstrate that multilevel evaluation improves the reliability that the proposed framework effectively balances performance and efficiency. This makes the framework suitable for practical hate
speech moderation systems that require accurate, generalizable, and explainable decisions.
Read the full abstract ▾
Artificial Intelligence
Progressive Skill Discovery as Access Control for Tool-Using LLM Agents: Structural Governance through Role-Scoped Capability Delivery
Michael Stettler, Benjamin Girardet, Jonas Canton et al. (+1 more)
Large
Language Model (LLM)
agents struggle to scale safely when exposed to vast enterprise toolsets. Providing an
agent with access to every internal tool leads to oversized context windows, degraded tool selection, and severe governance vulnerabilities - as system policies defined purely in
prompts remain probabilistic advice rather than hard constraints. Existing mitigations, such as multi-
agent domain delegation, decentralize audit logs and fail to guarantee policy compliance across sessions. We introduce skilder, a framework that packages capabilities into roles: bundles of skills, tools, and instructions, together with the limits that bound them. An
agent begins with a minimal role catalog, learns the roles a task requires, and receives each role's skills, instructions, and tools through a single MCP server. Because tools reach the
agent only inside learned skills, the same server enforces the scope of what was learned deterministically. We evaluate skilder against flat-context tool selection and multi-
agent orchestration across 13 tasks using six models (10 runs each). Our results show that, when models completed discovery and issued a governed call, the skilder simulated authorization layer enforced governance boundaries: no unauthorized tool call or parameter violation (e.g., a spending-limit breach) executed. Aggregate task pass rates also reflect whether each model followed the discovery protocol and satisfied response-quality checks; those misses are not authorization failures. Furthermore, by allowing
agents to dynamically acquire cross-role capabilities mid-task, skilder preserves problem-solving flexibility while providing hard system-level enforcement.
Read the full abstract ▾
All authors Michael Stettler, Benjamin Girardet, Jonas Canton, Nicolas Corod
Artificial Intelligence
Driving Epidemic Models with AI Agents: the Epydemix Agent Framework
Nicolò Gozzi, Ciro Cattuto and Alessandro Vespignani
Artificial Intelligence
agents based on large
language models provide convenient natural
language interfaces to scientific software, but reliability is not automatic. Here we introduce the Epydemix
Agent Framework, an additive layer over Epydemix, an open-source Python library for
stochastic compartmental epidemic modeling. The framework extends the library with four capabilities to facilitate interaction with an AI
agent: discovery of available models and parameters, preventive validation of a declarative scenario specification, execution through tested library code, and inspectability of results. These capabilities let an
agent handle the entire modeling process, from the natural-
language description of the scenario to quantitative results, figures, and interpretation of findings without writing custom code. Each step reads input files and saves results in a separate output bundle, making the process auditable and reproducible. First, we show the end-to-end workflow with a case study comparing vaccination strategies for a novel respiratory virus. Second, we assessed the framework across 50
agent sessions and five modeling tasks by comparing the
agent use of the framework against the direct use of the Python interface. The framework reduced turns, output
tokens, and cost on most tasks, unless it trades resources for per-point reproducibility.
Read the full abstract ▾
Artificial Intelligence
TRACER: Trajectory-Aligned Learning for Multi-Turn User Simulation
Geng Chen, Ruotong Pan, Zhirui Yang et al. (+9 more)
Faithful user simulation is fundamental to building, evaluating, and improving interactive AI at scale. Yet current simulators often produce plausible individual responses without reproducing the intent
evolution and outcomes observed in real interactions. We propose TRACER, a multi-turn user simulator that models evolving user intent and aligns simulated trajectories with real ones. TRACER is trained in two stages: supervised fine-tuning on real user dialogues, followed by multi-turn reinforcement learning. The RL stage combines hierarchical outcome- and trajectory-level rewards with deviation-aware advantage modulation, jointly addressing reward sparsity and credit assignment challenges in long dialogues. On real customer-service sessions organized into reference cohorts, TRACER-7B surpasses the strongest baseline by 11.4 conversion F1 points, while outperforming all baselines on group-level conversion-rate error and semantic trajectory distance and generalizing to out-of-distribution scenarios. In human Turing tests, annotators identified TRACER conversations at near-chance accuracy. Building on this simulator, we further introduce the Dynamic Marketing
Benchmark, which jointly evaluates persuasion and response quality via simulated interactions, revealing that higher response quality does not necessarily correspond to higher conversion rates.
Read the full abstract ▾
All authors Geng Chen, Ruotong Pan, Zhirui Yang, Qiqi He, Jiawei Chen, Zhang Yunfei, Chongyuan Chen, Minxuan Lv, Zheng Yang, Win-Bin Huang, Xiangyu Wu, Wenwu Ou
Artificial Intelligence · Cross-listed from cs.LG
Beyond Static Graph World Models: Learning Stochastic Latent Dynamics over Evolving Topologies
Alex Schutz, Nick Hawes and Victor-Alexandru Darvariu
Graph-based world models have recently emerged as a means of learning transitions over relational state representations. However, existing approaches are largely limited to fixed-
topology graphs or deterministic, fully observable environments. We propose the Graph Dynamics Model (GDM), a world model for graph-structured observations that is designed to handle the more general setting of evolving
topologies in
stochastic and partially observable environments. The GDM uses a sparse recurrent adjacency
matrix to model
topology updates and perform message passing, together with a recurrent state-space architecture for modelling
stochastic transitions. Furthermore, we identify a gap in the evaluation of graph-based world models, as existing methods do not provide a means of comparing predicted and true distributions over the joint graph state comprising the interdependent
topology, node features, and graph features. We therefore introduce the Graph Distribution Distance (GDD) metric, which uses maximum mean discrepancy with a graph kernel to comprehensively compare joint next-state distributions. We evaluate the GDM across several environments, including
stochastic and partially observable settings. We demonstrate that GDM outperforms baseline models and displays zero-shot generalisation on large graphs.
Read the full abstract ▾
Artificial Intelligence
Training Object Permanence in World Models
Haotian Zhang, Fengyuan Yu, Dezhi Luo et al. (+28 more)
Object permanence and solidity are hallmarks of human cognitive priors. Recent studies show that
video generation models, a paradigmatic class of current world models, have begun to show emerged
reasoning abilities, making them ideal candidates for building human-like physical intelligence. Do
video models have emerged object permanence in them? If not, could we train them with a core-cognition inspired
dataset? We introduce WROP (World
Reasoning with Object Permanence), a data infrastructure of 150 hand-designed cognitive science inspired tasks, divided into six cognitive categories. We build Blender generators that randomize speed, lighting, camera angle, and other nuisance parameters while preserving each task's cognitive structure, yielding 10,000+ samples per task. We release a 1.5M-sample training corpus and a 300-question exam. On this exam we evaluate 14
video models: 3 reference-to-
video, 7 edit, and 4 continuation, among which PWM-WROP, our 16B world model. In a blind pairwise Elo study, PWM-WROP ranks first among continuation models and third overall, behind only a statistical tie between two reference-to-
video models. We release the data, exam, model answers, scores, weights, and PWM, our native-PyTorch training stack on AWS Trainium2.
Read the full abstract ▾
All authors Haotian Zhang, Fengyuan Yu, Dezhi Luo, Haoran Sun, Zehong Zhao, Qingying Gao, Yihan Li, Siyuan An, Huayi Qin, Yilan Zhang, Zhengze Jiang, Pinyuan Feng, Renrui Zhang, Ziyu Guo, Letian Wang, Mengyue Yang, Kangfu Mei, Maijunxian Wang, Ran Ji, Vikash Kumar, Freda Shi, Chandra Sripada, Vincent C. Muller, Philip Torr, Alan Yuille, Nikolaus Kriegeskorte, Felix Juefei-Xu, Lvmin Zhang, Jieneng Chen, Yilun Du, Hokin Deng
Artificial Intelligence · Cross-listed from cs.IR
The Fellowship of the Query: Learning Retrieval Actions
Mohammed Al-Maamari, Saber Zerhoudi, Michael Granitzer et al. (+1 more)
Retrieval-augmented question answering requires control decisions about when to decompose a question, search, reformulate, extract evidence, synthesize facts, verify progress, and stop. We study whether trajectory fine-tuning can improve small
language models (SLMs) as next-action controllers. We additionally evaluate a low-resource setting in which a single SLM serves as both the controller and the final-answer generator. From accepted teacher search traces, we build a seven-way action-prediction task, where the model predicts the next structured teacher action from the current trajectory state, and evaluate LoRA-supervised fine-tuning across SLMs and xSLMs as controllers. On 1,646 held-out action examples, Granite 4.1 3B trained on 13,194 actions reaches macro-F1 0.6536, compared with 0.1736 for zero-shot prompting of the same model and 0.5399 for a TF-IDF logistic-regression baseline. In an end-to-end controller/generator swap evaluation over 149 held-out trajectories, using the fine-tuned model for both roles improves Exact Match from 0.7530 to 0.7946 and
token F1 from 0.7783 to 0.8295 compared with using the base model as both controller and generator. The cross-role conditions show that the fine-tuned controller increases evidence-fact recording when the generator is fixed, while controller-only final-answer gains are not statistically clear. Overall, trajectory supervision improves action prediction and evidence-recording behaviour in this evaluated pipeline. Code is available at https://github.com/padas-lab-de/
agent-action-controller
Read the full abstract ▾
All authors Mohammed Al-Maamari, Saber Zerhoudi, Michael Granitzer, Jelena Mitrović
Artificial Intelligence · Cross-listed from cs.CR
Decision Hijacking: Prompt Injection Attacks on Jev's Typed Probabilistic Decisions
Tiantong Wu and Wei Yang Bryan Lim
Most studies of
prompt injection focus on generative
agents, leaving their effects on models with schema-defined outputs unclear. We examine these effects in Jev, a non-generative decision model, using 510 reconstructed InjecAgent cases. Malicious content shifts action probabilities but rarely causes Jev to select the attacker's target. Override markers reduce this influence, while claims of contextual relatedness have small effects. Adaptive attacks using score feedback double the mean highest attacker-target probability found during optimization, while success on fresh validation calls rises from 1.8% to 3.5%. Exploratory analysis links these successes to small initial decision margins or greater attacker control over the observation. Together, these findings show that schema-defined outputs change but do not eliminate
prompt-injection risk, highlighting the need to evaluate how untrusted content influences choices within the allowed action set.
Read the full abstract ▾
Artificial Intelligence
Adversarial Closed-Loop Curriculum for Evolving Role-Playing Agents
Zheng Zhang, Liu Liu, Qi Chai et al. (+4 more)
Role-playing
agents based on large
language models have been widely applied in areas such as personalized assistance and social simulation. Recent RL methods typically train on a fixed scenario pool collected before learning begins. This creates a distributional bottleneck: as the
agent improves, the scenarios where it performs poorly also change, while the training distribution remains static. Therefore, we propose AdvRole, an
adversarial context rewriting framework that turns role-playing RL into a closed-loop curriculum. AdvRole alternates between an Actor that learns to role-play and a Rewriter that edits character profiles and dialogue contexts into actor-specific hard scenarios. The Rewriter is trained with a performance-gap reward, which favors rewrites that reduce the current Actor's score relative to the original scenario. As a result, the scenario pool evolves with the Actor and continuously targets under-mastered regions of the character-context space. Experiments on three role-playing
benchmarks covering English and Chinese, as well as a new
multilingual benchmark we release, show that AdvRole consistently outperforms baselines.
Read the full abstract ▾
All authors Zheng Zhang, Liu Liu, Qi Chai, Deheng Ye, Peilin Zhao, Mao Zheng, Hao Wang
Artificial Intelligence · Cross-listed from cs.LG
UO-FIE: Combining Exact-Label Supervision with Graded Utility for Factivity Inference
Xinchen Xiao
The Factivity Inference Evaluation 2026 (FIE2026) classifies Chinese context-hypothesis pairs into nine ordered factivity intervals. Its evaluation metric rewards both exact predictions and proximity to the correct interval, while 64.1% of the 566 training examples belong to a single class. In preliminary experiments, several mDeBERTa classification models predominantly predict the dominant class, whereas a Huber-regression baseline produces more predictions near the correct interval but fewer exact matches. We introduce Utility-Oriented Factivity Inference (UO-FIE), a parameter-efficient system that combines exact-label supervision with graded utility. UO-FIE predicts a distribution over the nine classes and combines hard-label supervision, utility-based soft targets, scheduled class weights, and an ordinal loss. We evaluate expected-utility decoding in controlled comparisons and use ordinal calibration selected on out-of-fold predictions for the submitted system. Based on Qwen3.5-9B with LoRA, UO-FIE ranks first in the fine-tuning track with a macro utility of 0.8316. A separate
prompt-based ensemble ranks third in the non-fine-tuning track with a macro utility of 0.8450.
Read the full abstract ▾
Artificial Intelligence · Cross-listed from cs.LG
Learning to Discover Interesting Mathematics
Niket Patel, Ahmad Rammal, Amaury Hayat et al. (+2 more)
Recently, Large
Language Models (LLMs) have been increasingly able to solve advanced mathematical problems, including many that have been open for decades. This opens the door to expansion of mathematical knowledge at unprecedented scale. Yet, while LLMs may be able to
conjecture and prove more and more
theorems, it remains open whether this new mathematical knowledge is interesting or useful. We define intrinsic interestingness of a
theorem as the ratio between the length of its proof and the length of its statement. We show that this correlates strongly with an extrinsic measure of the downstream utility of a
theorem. We identify the difficulty of a proof conditioned on a set of premises as a useful primitive for computing these metrics, and train a 27B model that predicts proof difficulty more accurately than frontier general-purpose models. Optimizing for our metric creates a model capable of producing more interesting
theorems, while also reducing substantial or full overlap with Mathlib from 91.9% to 30.6%, showcasing the creation of more out-of-distribution math. We show that our system can generate candidate
theorems, select the most interesting among them, and iteratively build on a self-expanding mathematical library. These metrics provide a practical and quantifiable signal for ranking
conjectures and guiding proof search within formal mathematical libraries. Our framework provides a path towards self-expanding, machine-verified mathematical libraries that can choose worthwhile statements without relying on human-supplied targets.
Read the full abstract ▾
All authors Niket Patel, Ahmad Rammal, Amaury Hayat, Remi Munos, Julia Kempe
Artificial Intelligence · Cross-listed from stat.ML
NumericJev: Jev-like LLM Numerical Decoding with Multiway Decision Trees
Weiwei Ye, Hangchen Liu and Renhe Jiang
Large
language models can interpret natural lan- guage, yet robust decisions remain challenging. Jev-like models expose structured choices, but these interfaces do not directly provide numeri- cal values at a requested precision. We propose NUMERICJEV, a training-free numerical decod- ing
algorithm that enables numerical output from any LLM with a Jev-like structured-choice in- terface. Surprisingly, on our arithmetic bench- mark, it outperforms direct selection from a can- didate list containing the correct answer by 2.93 percentage points (Figure 1). Our motivation comes from the observation that numerical range selection is itself a decision problem that Jev- like LLMs can address. NUMERICJEV recur- sively refines a range through a multiway deci- sion tree while retaining the original question in context, without parameter updates or hidden- state access. On a 100-value grid, a ten-way tree requires only two decision rounds. Range- normalized MAE is 1.84% versus 5.18% for di- rect choice. A separate three-date historical- index study yields 4.58% mean relative recall er- ror and 0% readout error when the value is sup- plied. Code is available at https://github. com/Bring-AI/jev-numeric.
Read the full abstract ▾
Artificial Intelligence · Cross-listed from cs.CR
Persistent Billable State: Denial-of-Wallet Attacks and Defenses in Tool-Calling LLM Agents
Jinqian Zhang, Haojun Xia, Shujiang Wu et al. (+4 more)
Multi-step tool-calling LLM
agents rely on host runtimes to preserve state across turns. When a runtime carries an external tool return into later model inputs, providers meter it again. An admitted malicious or compromised tool can thereby convert untrusted data into recurring victim-billed processing without victim credentials or local runtime privilege. We call retained content persistent billable state and formalize the host's decision over whether and how it enters later billable context as the persistent billable-state boundary. We present the first systematic security study of this post-admission lifecycle. We derive six denial-of-wallet attack vectors and build DOW-BENCH, an end-to-end harness evaluated across six model families. Across 243 executions, usage telemetry shows that the maximum per-session cumulative input reaches 14,293x the session's first-call input. Controlled history-policy reruns isolate raw retention's contribution: retaining raw history increases mean effective session cost by 21.2-35.9%. Compression succeeds on 10/12 and 11/12 history-dependent tasks, versus 2/12 under deletion for each provider. To govern this boundary, we combine deterministic history transformation with four host-side invariants that bound
prompt mass, context growth, recursive opportunity, and cumulative spend before reingestion. The kernel contains every recurring attack in the 123-evaluation replay corpus. Across 24 Mistral Small 4 workflows, a progress-authorized policy achieves 22/24 oracle-verified task successes with no pre-completion interruptions, versus 13/24 under a fixed cap. Only 71 of 3,830 scanned MCP server and transport repositories expose any code-visible safeguard proxy, and none cover all four safeguard families. These results establish persistent billable state as a first-class security object and pre-reingestion as its host-owned control point.
Read the full abstract ▾
All authors Jinqian Zhang, Haojun Xia, Shujiang Wu, Jingkun Yue, Xia Zhang, Zhangpei Cheng, Bibo Tu
Artificial Intelligence · Cross-listed from cs.LG
SGA: Uncertainty Quantification for Multi-Step Forecasting in Time Series Foundation Models
Xin-Yu Hu, Shuang Liang, Cheng Feng et al. (+1 more)
The recent emergence of Time Series Foundation Models (TSFMs) has significantly advanced multi-step
forecasting performance, enabling accurate predictions over extended future horizons. However, existing TSFMs often suffer from significantly inherent uncertainty, which typically manifests as derived forecast branches emerging at each time step and spreading to subsequent steps; different forecast branches often exhibit varying
forecasting performance, thereby undermining the credibility of TSFM forecasts. In this paper, we propose the Slicing-Graphing-
Alignment (SGA) method to quantify the uncertainty of multi-step TSFM forecasts. The proposed SGA first characterizes the
topology of all potential forecast branches using a directed acyclic graph, such that the graph complexity bounds the uncertainty of multi-step forecasts, and then precisely measures the graph complexity by integrating both topological information and TSFM-inherent stochasticity. Experimental results conducted on 11 TSFMs and 27
datasets demonstrate that (i) SGA achieves the best performance when ranking predictive errors with uncertainty estimates; (ii) SGA works with a more extensive and more precise sampling coverage than those of existing UQ methods, deriving a quantification mechanism fundamentally different from those of established ones; and (iii) larger model scales of TSFMs correlate with lower uncertainty estimates of multi-step forecasts, suggesting another empirical scaling law for uncertainty quantification of multi-step TSFM forecasts.
Read the full abstract ▾
All authors Xin-Yu Hu, Shuang Liang, Cheng Feng, Shao-Qun Zhang
Artificial Intelligence · Cross-listed from cs.LG
Auditability Is Not One Property: Rule Overlap, Behavioural Agreement, and Composition in Reinforcement Learning
Liu Hung Ming
Reinforcement-learning (RL) policies are often distributed as opaque neural checkpoints, while training logs show that a run occurred without explaining what the policy learned. We study whether independently trained policies can be represented and composed through auditable discrete behavioral rules. We define auditability as six separately testable predicates: trace integrity, lossless coding, rule coverage, behavioral agreement, composition quality, and value-model reliability. Our protocol uses a shared frozen symbolizer, passive rule extraction, an append-only hash-bound ledger, exact environment replay, and offline confidence-ranked arbitration with an explicit blind-spot fallback. The results place strict limits on this description layer. Rule-set overlap does not imply behavioral agreement: policies may share symbolic rules while choosing near-chance-matching actions on fresh states. The fused policy therefore selects among existing rules rather than generating a new skill. On a conflict-dominated task, an apparent fusion failure is traced to an induction/deployment mismatch: rules induced from sampled actions were evaluated under argmax actions, and deployment-consistent re-induction reverses the arbitration ordering. A fitted-Q generalized-policy-improvement diagnostic also fails in both environments, limiting claims that rule fusion is superior to value-based composition. One exploratory comparison favors rule fusion, but its comparator is post hoc, the task is partly saturated, and the fused policy remains below the strongest held-out actor. We contribute an evidence-bounded audit and composition protocol, not a claim of universal interpretability or autonomous skill generation. Future work must add temporally extended skills, cross-skill interfaces, composition search, and independent novelty audits.
Read the full abstract ▾
Artificial Intelligence
TWIST: A Proposed Benchmark for Intervention Quality in Conversational Memory, with a Human-Validated Draft-Alignment
Subrat Panda
Long-conversation
memory benchmarks increasingly test recall and prompted knowledge updates, and recent work studies evolving user beliefs and
memory state. TWIST is a proposed
benchmark suite for a complementary, unmeasured property: intervention quality – whether a deployed
memory system, exercised through its own ingest/recall/vet surface, acts correctly at belief change points. Four tracks cover unprompted tension detection, vetting outgoing drafts against the record, answering with current beliefs while preserving supersession history, and governing sensitive recall. The suite extends LoCoMo's corpora and harness, pairing every detect/block metric with a matched do-not-over-detect control: surface-matched hard negatives price false intervention, so no track can be gamed by flagging everything. The
benchmark itself is validated first: independent, gold-blind double annotation with adjudication, judge decoy calibration, and a separability audit. On the human-validated Track B v1.0 key (161 items, post-adjudication kappa = 0.85), no tested configuration simultaneously achieves high contradiction recall, high hard-negative specificity, and high attribution: flat-RAG baselines detect 0.76-0.97 of true contradictions but falsely flag 16-43% of surface-matched safe drafts depending on backend, while a deployed coherence-oriented system almost never over-flags (0.98-1.00 specificity) yet catches 42% of true contradictions – a trade-off no recall-only score can see. A 13-configuration baseline ladder localizes causes: every gold contradiction is detectable from its evidence alone (recall 1.000), calibrated models nearly solve the track given the full transcript – consistent with substantial
retrieval-coverage gaps – and draft-only floors reveal model-dependent style priors. A system's TWIST profile, beside its recall score, measures whether
memory knows when to intervene and when not to.
Read the full abstract ▾
Artificial Intelligence · Cross-listed from cs.CR
Where Cyber Agents Struggle: Bottleneck Analysis of Multi-Stage LLM Agents
Saeedeh Lohrasbi, Mohammad Mamun, Ahmed Yehia et al. (+2 more)
Multi-stage LLM-based cyber
agents may complete attack workflows while remaining brittle, costly, or reliant on incorrect interpretations of execution evidence. Success rates alone obscure inefficiency, adaptation through retries, and recognition of success or failure. We present an end-to-end diagnostic study of an Autonomous Adversary system with orchestrator, executor, and validator LLMs in enterprise-like lateral-movement scenarios. Six frontier models are evaluated across two scenarios and three modes: expert-defined, self-scaffolded, and fully autonomous. We assess validator consistency and evidence grounding; introduce a subtask-conditioned, cost-aware score for abnormal
token use, retries, and runtime; and use comparative LLM-as-a-Judge analysis to identify planning deficiencies, including tool misalignment, plan similarity, over-specification, inadequate probing, and weak recovery. Validators are generally relevant and evidence-grounded but often nonspecific and overly optimistic. Bottlenecks cluster in credential and lateral-movement tasks, spread with scenario complexity, and vary more under full autonomy. Reliable evaluation must assess outcomes, evidence interpretation, resource use, and adaptation after failure.
Read the full abstract ▾
All authors Saeedeh Lohrasbi, Mohammad Mamun, Ahmed Yehia, Scott Buffett, Sherif Saad
Artificial Intelligence
DEEPO: Dual-Entropy Enhanced Policy Optimization for Hallucination in MLLMs
Yingxuan Zhuang, Miao Pan, Wangjie Gan et al. (+6 more)
Reinforcement learning (RL) is widely used to sharpen
reasoning in
multimodal large
language models (MLLMs), yet its effect on
hallucination is uneven. We trace this to two weak points in the correction chain from reward to parameter update. At the rollout level, hard queries—those with high semantic
entropy—frequently produce unanimously wrong sample groups, collapsing the group-relative advantage to zero exactly where
hallucination risk is highest. At the optimization level, confident-but-wrong
tokens are gradient-invisible: a categorical policy's expected score-gradient norm vanishes as its distribution sharpens, so the predictions that most need correction receive the weakest updates. We propose Dual-
Entropy Enhanced Policy Optimization (DEEPO), a dual-stage enhancement combining signal variance regularization with gradient preconditioning: semantic-
entropy-triggered expert prefixes inject grounded continuations on high-uncertainty queries, providing direct supervision and restoring advantage variance, while advantage-sign-aware Renyi preconditioning counteracts logit-level saturation so correction reaches confident errors in the operational confidence regime. Both branches improve over GRPO individually; their interaction is statistically significant on VideoMMMU—the most complex long-horizon task in our evaluation suite (+4.0$, 95% CI [1.1, 6.9])—and additive elsewhere. DEEPO reduces
hallucination while preserving accuracy and training stability.
Read the full abstract ▾
All authors Yingxuan Zhuang, Miao Pan, Wangjie Gan, Jingxiao Yang, Fan Wang, Weiming Liu, Cheng Tan, Xuhong Zhang, Jintao Chen
Artificial Intelligence · Cross-listed from cs.LG
When Explanations Cannot Be Read: Measuring and Correcting SHAP and LIME Rendering for Right-to-Left Languages
Rameesha Zia and Muhammad Shahid Iqbal Malik
Post hoc explanation methods such as SHAP and LIME are widely used to interpret text classifiers, but their visualizations are mainly designed for left-to-right
languages. When applied to right-to-left (RTL)
languages such as Urdu, Arabic, Persian, and Hebrew, the attribution values remain mathematically valid, while their visual presentation fails.
Tokens appear out of sequence, connected letterforms break apart, and plot layouts do not follow the natural reading direction. This study addresses this gap as a visualization problem rather than a limitation of the explanation methods themselves. We present SHAP-RTL, a rendering layer that corrects reading direction and script shaping in SHAP and LIME visualizations, with per-
language font selection, while preserving the original attribution values, feature ordering, and model outputs. The approach is evaluated on Urdu, Arabic, Hebrew, and Persian hate and offensive-
language datasets using TF-IDF and logistic regression classifiers. Rendering correctness is measured by an OCR round trip over 200 feature words per
language. Default rendering yields character error rates of 0.820 to 0.979, meaning the label no longer carries its
token; the common reshape-and-reorder workaround fails for Urdu at 0.998, worse than no correction; and the Matplotlib 3.11.0 text rewrite inverts that workaround, while SHAP-RTL remains correct under both versions. The framework also verbalizes the same attributions as short contextual explanations in the reader's
language, constrained to the identified features. Evaluation in this paper concerns rendering correctness; assessment of the generated explanations is left to future work. The study highlights the importance of
language-aware visualization in making post hoc explainability more accessible across different writing systems.
Read the full abstract ▾
Artificial Intelligence · Cross-listed from stat.ML
Speculative Evaluation of Stochastic LLMs
Qianli Shen, Xiang Li, Ruomeng Ding et al. (+3 more)
Evaluating a
stochastic large
language model is costly:
benchmark scores estimate expected performance from randomized rollouts, yet uniform repetition ignores sharp differences in task-level rollout variance. We ask how to minimize the variance of a fixed-
benchmark mean under an exact rollout budget. We develop Speculative Evaluation with a Hierarchical
Bayesian Neyman (HBN) policy with pilot size and stage weight jointly chosen ex ante. It runs a short uniform pilot, pools per-task success counts with a hierarchical
Bayesian model, and uses posterior expectations of task-level sampling variances for exact positive-integer Neyman allocation. To mitigate the pilot synchronization barrier, HBN-async speculatively executes continuations from partial pilot feedback and retains those selected by the final allocation. Across six checkpoints and 18
benchmark groups, we evaluate 107 nondegenerate
benchmark-checkpoint profiles. For rollout budgets of 8-64 per task, Speculative Evaluation reduces variance relative to Uniform by 12.8%-33.6% on average across profiles, outperforming hindsight-tuned empirical and independent
Bayesian baselines. Real-generation experiments that account for the pilot synchronization barrier show that HBN-async mitigates its overhead, helping translate statistical efficiency into practical evaluation benefits.
Read the full abstract ▾
All authors Qianli Shen, Xiang Li, Ruomeng Ding, Yanxi Chen, Daoyuan Chen, Yaliang Li
Artificial Intelligence · Cross-listed from cs.CR
Who Is Behind the Harness? Fingerprinting LLMs through Agentic Behavior
Chuyi Wang, Xiaohui Xie, Tongze Wang et al. (+2 more)
LLMs increasingly operate through coding-
agent harnesses that inspect repositories, invoke tools, and modify files. Substituting the model behind such an
agent can therefore change security-relevant decisions, including whether it verifies changes or recovers safely from failures. Existing LLM fingerprints largely infer identity from direct text or
token distributions. In coding
agents, these signals are mediated by system instructions, controller logic, tools, and execution feedback, limiting their transfer. We present
LIDAR (LLM Identification from Decisions and Actions at Runtime), an active black-box fingerprinting method for coding-
agent execution. Three coding probe pairs expose post-edit verification, transient-failure recovery, and specification–test conflict resolution under controlled changes.
LIDAR represents the resulting trajectories with complementary instance-level and distribution-level features and compares them with clean references using a lightweight probabilistic identifier. It requires no access to model weights, logits, or provider internals. Across 36 models from seven families and two
agent harnesses,
LIDAR achieves high Top-1 accuracy and MRR and outperforms four existing fingerprinting and API-
auditing baselines. Ablations confirm that the two feature levels, all probe pairs, and their controlled variants contribute. These results show that
agent execution behavior provides model-identity evidence beyond final outputs.
Read the full abstract ▾
All authors Chuyi Wang, Xiaohui Xie, Tongze Wang, Fangchen Luo, Yong Cui
Artificial Intelligence
BaseCamp — An Agentic AI Framework for Automating DNA Sequencing Data Pipelines
Eranga Bandara, Xueping Liang, Asanga Gunaratna et al. (+19 more)
DNA sequencing pipelines, spanning quality control,
alignment, variant calling, and annotation, are now reliably executed by workflow management systems that orchestrate established bioinformatics tools at scale. What remains manual is the decision layer surrounding that execution: selecting quality thresholds appropriate to a sample and platform, adjudicating borderline variant calls, diagnosing anomalies, and determining which findings warrant expert review. These decisions are repetitive, judgment-intensive, inconsistent across operators, and frequently undocumented. This paper introduces BaseCamp, a novel agentic AI framework for automating the decision layer of DNA sequencing pipelines. The framework decomposes the pipeline into six specialized AI
agents, covering sample intake and quality control,
alignment, variant calling, annotation, cross-stage monitoring, and reporting. Critically, BaseCamp
agents do not perform sequence analysis: established tools execute
alignment, calling, and annotation, while the
agents select among them, configure them, interpret their output, and decide what follows. This confines
language model
reasoning to the judgment layer where it is reliable and preserves the reproducibility existing tooling guarantees.
Agent reasoning is powered by a consortium of fine-tuned, domain-specialized large
language models coordinated by a central
reasoning LLM, executing locally so no sequencing data leaves the operating environment, under human-in-the-loop orchestration. Evaluation shows
agent-generated configurations are concordant with expert practice, that an explicit filtering ledger renders inspectable what filtering otherwise removes without trace, and that cross-stage anomaly detection surfaces conditions execution monitoring misses. BaseCamp offers a generalizable blueprint for agentic automation of scientific data pipelines.
Read the full abstract ▾
All authors Eranga Bandara, Xueping Liang, Asanga Gunaratna, Tharaka Hewa, Abdul Rahman, Peter Foytik, Safdar H. Bouk, Sachini Rajapakse, Isurunima Kularathna, Pramoda Karunarathna, Chalani Rajapakse, Ng Wee Keong, Kasun De Zoysa, Amin Hass, Wathsala Herath, Ross Gore, Ravi Mukkamala, Nihal Siriwardanagea, Gihan Siriwardanagea, Aruna Withanage, Nilaan Loganathan, Sachin Shetty
Artificial Intelligence
Pistis Technical Report
Heyun Chen, Xiaohan Lan, Jiaxi Li et al. (+17 more)
We introduce the Pistis model family, comprising 27B- and 9B-parameter
multimodal large
language models built on Qwen3.6 and Qwen3.5, respectively, and developed through a general and scalable post-training framework. The framework first establishes a strong foundation through large-scale
multimodal supervised fine-tuning (SFT). Building on this SFT foundation, we propose Interleaved
Distillation and Reinforcement Learning (IDRL), a novel post-training paradigm that tightly integrates on-policy
distillation and reinforcement learning within a single training loop. By alternating between the two objectives, rather than optimizing either in isolation or combining them in a static joint loss, IDRL enables more effective knowledge transfer, greater optimization stability, and more precise credit assignment for long-horizon agentic trajectories, leading to stronger performance while mitigating common capability trade-offs. At both model scales, the framework produces two specialized variants: Pistis-Thinking, designed to strengthen deep
multimodal reasoning, and Pistis-Agentic, which additionally incorporates agentic trajectory data to support long-horizon planning, iterative
reasoning, and tool use. Pistis-Agentic is particularly strong in
multimodal search. Both scales outperform their corresponding base models. Beyond model-parameter optimization, we further introduce Pistis-Auto-Harnessing (PAH), a system-level method that automatically improves the
agent's inference harness through iterative optimization. Experiments demonstrate that PAH enhances the model performance without updating the model parameters or increasing the interaction budget.
Read the full abstract ▾
All authors Heyun Chen, Xiaohan Lan, Jiaxi Li, Zhilin Lu, Qi She, Weiwen Xu, Fei Yu, Yujie Zhong, Jinghuan Chen, Zijian Feng, Siyu Jiao, Yiheng Lin, Xinhao Wang, Sihan Yang, Jieyu You, Changbin Zhang, Hengyu Zhang, Xudong Zhang, Yunqing Zhao, Shuai Zheng
Artificial Intelligence · Cross-listed from cs.LG
SMILESGNN: Interpretable Clinical Toxicity Prediction via SMILES-Graph Cross-Attention Fusion
Quang Minh Nguyen, Thuy Quynh Nguyen, Duc Minh Le et al. (+3 more)
Drug toxicity prediction is critical for reducing late-stage attrition in drug discovery, yet remains challenging due to severe class imbalance, scaffold-based generalization, and the clinical need for interpretable predictions. Single-modality approaches-SMILES
Transformers or graph neural networks capture complementary aspects of molecular structure, while sequence-only models cannot directly provide graph-attributed explanations. We present SMILESGNN, a
multimodal architecture that fuses a SMILES
Transformer encoder and a GATv2 graph encoder via cross-
attention, and SMILESGNN-PT, a variant using a ChemBERTa-2 pretrained backbone. The design retains an explicit graph branch within the predictive pipeline, supporting GNNExplainer-based analysis of substructures associated with toxic predictions. On ClinTox, SMILESGNN achieves AUC-ROC 0.987 and F1 0.906 with only 0.4M parameters, performing competitively with a strong SMILESTransformer and a larger ChemBERTa-2/GATv2 concat-fusion baseline. On Tox21 (12 tasks), SMILESGNN-PT obtains mean AUC-ROC 0.750, comparable to ChemBERTa-2 alone and the same-backbone concat-fusion baseline. Overall, the results suggest that cross-
attention is a practical fusion alternative that preserves competitive predictive performance while enabling graph-based interpretability support.
Read the full abstract ▾
All authors Quang Minh Nguyen, Thuy Quynh Nguyen, Duc Minh Le, Ho Nhat Minh Nguyen, Thanh Long Dai Doan, Trong Nghia Nguyen