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Computer Science

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Monday 12 October

Cryptography and Security

Who is the 'I' in AI? Establishing Decentralized Identity and Accountability for Autonomous Agents

Ahammod Munim Ramil, Khalid Al Kafi, M. A. Abdullahis Sany et al. (+4 more)

The rapid proliferation of autonomous AI agents in decentralized ecosystems challenges existing identity paradigms. While human identity is traditionally anchored in biological and/or self-asserted attributes and government credentials, AI agents lack a standardized definition of selfhood, creating critical gaps in trust and accountability. This paper introduces a novel framework for establishing Decentralized AI Identity rooted in Self-sovereign identity (SSI) principles. We propose a formal model that clearly distinguishes human users and AI agents, enabling distinct verification logic for each class while maintaining a unified identity calculus. The central to our contribution is the ABCD Framework, a multi-layered approach to agent identification and verification: (A) Algorithmic Fingerprint, which binds the identity to static model architectures and provenance; (B) Behavioural Fingerprint, which verifies dynamic performance metrics over time; (C) Cryptographic Identity, which anchors the agent via Decentralized Identifiers (DIDs) and (D) Delegation, which cryptographically binds agent actions to respective human accountability. By orthogonalizing these dimensions, we model how systems can enforce granular trust policies ranging from simple authentication to complex liability assignment, thereby solving the black box identity problem in the autonomous economic activities of AI agents.
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All authors Ahammod Munim Ramil, Khalid Al Kafi, M. A. Abdullahis Sany, Muktadirul Islam Nibir, Md Yeasin Ali, Mohammad Zavid Parvez, Md Sadek Ferdous

Computation and Language

ToolHACE: A Dataset for Detecting Hallucinations in Tool-Based LLM Responses

Nikolay Ivanov, Sergey Pletenev, Elisei Rykov et al. (+8 more)

Tool-augmented language models ground their responses in external tool outputs, improving reliability beyond parametric knowledge alone. However, even when the correct tool is called and returns accurate information, models may still contradict such outputs, omit critical details, or generate unsupported claims. Existing hallucination benchmarks for tool-augmented dialogue primarily focus on the tool-calling stage, evaluating whether the correct tool and arguments are selected, while overlooking errors that arise during post-tool response generation. To address this gap, we introduce ToolHACE, a span-level benchmark for hallucination detection in tool-augmented LLM responses, covering five fine-grained hallucination categories. Through extensive experiments with modern hallucination detectors, we show that post-tool-call hallucinations are widespread and remain challenging to detect. Our results further demonstrate that effective detection requires training on specialized tool-augmented corpora, while transfer from tool-agnostic hallucination datasets performs poorly in this setting. These findings highlight the need for dedicated benchmarks and models for reliable hallucination detection in tool-augmented language systems.
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All authors Nikolay Ivanov, Sergey Pletenev, Elisei Rykov, Anatolii Frolov, Bogdan Monogov, Alexander Potekhin, Maria Marina, Daniil Moskovskiy, Viktor Moskvoretskii, Alexander Panchenko, Ilseyar Alimova

Computation and Language

D2: High-Capability Full-Duplex Spoken Language Models from Half-Duplex Backbones

Cheng-Kuang Chang, Alexander H. Liu, Brian Kingsbury et al. (+5 more)

Full-duplex spoken language models (FD-SLMs) support simultaneous listening and speaking, but often lag behind half-duplex spoken language models (HD-SLMs) in general capabilities. We propose Dialogue-to-Duplex (D2), a framework that converts pretrained HD-SLMs into high-capability FD-SLMs at fine temporal granularity. D2 adapts the original audio encoder to streaming through decoder-free representation distillation and learns full-duplex interaction through supervised fine-tuning. We systematically study the effects of chunk size on full-duplex interaction, general capabilities, and inference computation. We apply D2 to two distinct HD-SLM architectures, Qwen3-Omni and LLaMA-Omni 2. At a 12.5Hz update rate, Qwen3-Omni-D2 achieves 99.2% turn-taking success with 521-ms latency, 99.5% interruption success, and a state-of-the-art VoiceBench score of 70.1.
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All authors Cheng-Kuang Chang, Alexander H. Liu, Brian Kingsbury, Hilde Kuehne, Rogerio Feris, Samuel Thomas, James Glass, Kai-Wei Chang

Systems and Control

Coordinated Conservation Voltage Reduction and Network Topology Reconfiguration in Active Distribution Networks

Rida Fatima, Hassan Zahid Butt and Xingpeng Li

Distribution networks rely on conservation voltage reduction (CVR) and network topology reconfiguration (NTR) to manage demand, distributed energy resources (DERs), and tighter operating margins. Yet these strategies are typically deployed independently, despite coupled effects on feeder voltages, losses, and loading. This paper addresses that gap by developing a day-ahead mixed-integer second order cone programming (MISOCP) framework that jointly optimizes feeder head voltage control and network reconfiguration. The formulation incorporates AC branch flow constraints, ZIP load models, DERs, battery energy storage systems, and multi substation radiality. Using modified IEEE 33 bus and 123 bus systems, four configurations isolate the individual and coordinated effects of CVR and NTR. On the 33-bus system under mixed ZIP loading, NTR reduces active energy loss by 2.50%, while coordinated operation reduces it by 2.91% relative to SDN and produces a 2.46% CVR induced active load reduction relative to SDNTR. Under constant impedance loading, coordinated operation reduces active energy loss by 17.65% relative to SDN, while CVR reduces active load by 14.61% relative to SDNTR. DER portfolio scaling shows a nonmonotonic coordinated load reduction response, strongest at 20% penetration and decreasing at higher levels. On the 123 bus system, NTR reduces losses by 19.3%, while CVR reduces voltage dependent load energy by 2.3 to 2.4%. Under loading stress, SDN becomes infeasible at 120% loading, whereas CEDNTR remains feasible. Overall, the results demonstrate complementary, operating condition dependent benefits shaped by load composition, DER portfolio scaling, feeder topology, voltage margins, and source constraints, highlighting the value of joint optimization.
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Machine Learning

Can Rényi Accountants Beat Lossless Accountants for DP-SGD?

Jinho Bok

Recent frameworks in differential privacy (DP), such as privacy loss distributions and $f$-DP, provide lossless representations of the privacy leakage of mechanisms. These approaches are often considered to yield tighter $(\varepsilon, \delta)$-DP guarantees than those obtained from relaxed notions such as Rényi DP. But is that always the case? We study this question for differentially private stochastic gradient descent (DP-SGD) with fixed-size batches under replace adjacency. Our key observation is that in this setting, privacy accounting based on Rényi DP can use a pair of worst-case distributions (i.e., a dominating pair) that differs from the one used in the lossless frameworks. In particular, we characterize the worst-case distributions as two-dimensional Gaussian mixtures and derive an improved Rényi DP bound for DP-SGD. Numerical and asymptotic comparisons show that across various hyperparameter settings, a Rényi DP accountant based on the new bound can yield better privacy guarantees than those obtained from the lossless frameworks.
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Artificial Intelligence

When LLM Agents See a Conflict but Still Proceed: Cross-Domain Dependencies in Network Slicing

Marcos Lima Romero and Ricardo Suyama

Future AI-native networks are being designed to apply autonomous agents built on large language models (LLMs) in network management through intents. When intents from different domains meet on the same resource, an agent must recognize the cross-domain dependency before taking an irreversible action. We investigate this problem through a smart-grid use case, in which a power utility holds an active intent on a backup slice for protection traffic. This slice appears idle by design, which is precisely the profile targeted by a cost-reduction intent. We built a tool-based environment in which the dependency must be either queried by the agent or read from an inventory annotation, together with a negative control, and evaluated ten models from four different providers in 600 runs. Most models acted against the dependency they had observed: seven of the ten models removed the protection slice at least once after receiving the cross-domain dependency, and in the high-harm tier the slice was removed in 76% of the exposed runs, while four models practically never did. Only one of ten models never removed the slice. We identify two failure modes, not querying the dependency and querying it but proceeding anyway, and show that agents often reported no conflict after observing it. These results indicate that, in AI-native networks, visibility of cross-domain dependencies is not a sufficient safeguard, and that dependency verification should be enforced by the management architecture before irreversible actions rather than delegated to the LLM's judgment.
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Computation and Language

Gluing Local Contexts into Global Meaning: A Sheaf-Theoretic Decomposition of Transformer Representations

Bryce Grant and Peng Wang

Existing interpretability tools do not test whether a representation keeps the same content when only the phrasing changes. We treat each paraphrase pair as a local witness that two activations should agree and build a cellular sheaf over the paraphrase graph; its Laplacian $L_\mathcal{F}$ answers the question on a frozen model with no training. The kernel $H^0$ is a content subspace: at 20 dimensions it beats the preserved subspace of the concept eraser LEACE on held-out CounterFact fact retrieval across eight architectures by a mean $+17.1$\,pp, and constraining all paraphrases of a fact jointly adds a further $+6.5$\,pp. The spectral complement concentrates causal influence: ablating the most paraphrase-variable coordinates, a coordinate-wise proxy for it, changes model outputs $5.6$ to $26.5\times$ more than ablating coordinates of matched per-coordinate variability, across nine models from 124M to 13B parameters. Stacking layers into a grid creates $b_1 = N(L-1)$ algebraic 1-cycles, and the resulting model-derived harmonic mass varies by nearly three orders of magnitude across six architectures and ranks them as their steering fragility does (Spearman $\rho = 0.90$, exact one-sided permutation $p = 0.014$). The same paraphrase graph yields the content subspace, the influence directions, and the fragility measure. Project page: https://cwru-aism.github.io/gluing-lc-page/
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Robotics

D-World: An Explicit World-Language-Action Model with Recursive Self-Improvement under Dual Control

Xuan Wen and Baichao Wang

Frontier large language models (LLMs) now reason about space well enough to program robots, yet the world models behind robot policies are implicit, held in network weights or predicted video, and cannot be inspected or corrected directly. We present D-World, an explicit world-language-action model built on the Sense, Plan and Act decomposition. A perception agent writes the scene as an executable digital twin using generated vision and geometry code. A planning agent searches for actions in simulation and, after each executed action chunk, revises the twin's object states and named physical parameters against what the robot measured. A parameter revision is accepted only if replaying the latest chunk does not increase any measured residual beyond its tolerance, and every chunk is checked in the updated twin before it runs. Structural mismatches return to perception for reconstruction. We call this measurement-grounded cycle of correction and revision recursive self-improvement (RSI), in which the LLM stays frozen while the world it acts in improves. We model the choice of actions that both advance the task and expose model error as a dual control problem and show that the cost of incorporating evidence into the twin sets the threshold at which probing pays, so cheap local revision counteracts the turn-off of cautious controllers. Each episode runs a real-to-sim-to-real loop, and its recorded twins can be refitted and re-rendered offline into higher-fidelity environments for post-training. Because perception and planning communicate only through the twin and language, either can be replaced by a learned model, and since inference dominates the loop, the loop speeds up with faster models. On a dual-arm robot, towel and toaster tasks show local revisions, such as one reducing the bread's rendered-depth residual from 22.4 to 1.5 mm, that let execution continue without reconstruction.
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Computation and Language

Calibrated Typed Decisions for Entity, Relation, and Event Extraction

Yang Wang, Hanlin Xiao, Yanan Ma et al. (+2 more)

Information extraction models often perform well on the datasets they are trained on, but transferring to new domains and annotation guidelines remains challenging. Supporting user-specified types alone does not resolve this problem. We present CalOn, a unified 0.6B-parameter encoder for entity, relation, and event extraction across datasets and schemas. The model jointly encodes text and type definitions, then directly identifies entity spans, relations between spans, and event triggers and arguments. Its extraction decisions consider the requested types together, using their combined evidence to identify relevant candidates before assigning a type. This gives all three tasks a common probabilistic formulation while keeping every extracted mention anchored to the input text. We train on diverse sources, and supplement supervised learning with a reward that encourages correct extractions and penalises incorrect ones. Across 40 benchmarks, CalOn outperforms the evaluated baselines on most benchmarks. It leads all reported relation extraction benchmarks and achieves the highest average F1 for event triggers and arguments with gold triggers. The same model also performs strongly on aspect sentiment triplet extraction and pronoun resolution through its span-pair formulation. A correction for the probability bias introduced during training improves confidence estimates for entities, relations, and event arguments after calibration, with little change in F1.
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All authors Yang Wang, Hanlin Xiao, Yanan Ma, Chenghao Xiao, Chenghua Lin

Computational Geometry

The Rhythm of the Tiles: Narrow and Low Temperature Computation and Simulation in Synchronous Tile Self-Assembly

Florent Becker, Phillip Drake, Benjamin Durbin et al. (+3 more)

In this paper, we prove that the synchronicity provided by the syncTAM can be leveraged to perform two additional feats impossible in the asynchronous aTAM. The aTAM has long been known to be equivalent to finite automata in the one-dimensional case. For our first set of results, we prove that this still holds when a finite, fixed amount of space is available in the second dimension. Yet, surprisingly, in the synchronous setting, a small, fixed, width is enough to simulate a Turing Machine. This holds even in the non-cooperative setting. For our second set of main results we prove that there exists a single set of syncTAM tiles that can be used to simulate any syncTAM system simply by using those tiles to create a scaled version of the seed structure of the original system and then letting the tiles from that set self-assemble from the scaled seed structure. While it has been shown (FOCS 2012) that such a tile set also exists in the aTAM for simulating all aTAM systems, that "intrinsically universal" (IU) tile set requires cooperativity (and is impossible without cooperativity as shown in SODA 2014). We prove there exists an IU tile set in the syncTAM that works without cooperativity. Furthermore, there exists a tile set that is IU for the class of directed systems (i.e. systems which each have a single terminal assembly) while remaining directed during simulation. This was shown to be impossible in the aTAM (FOCS 2016).
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All authors Florent Becker, Phillip Drake, Benjamin Durbin, Nicolas Ollinger, Brent Orlina, Matthew J. Patitz

Software Engineering

AIPOCH Open-Science: An Open-Source AI Research Workbench with Reproducible .science Packages

Wen Ouyang, Bocheng Huang, Qianyu Yao et al. (+76 more)

AI-assisted research leaves instructions, computations, artifact revisions and checks whose relationships matter when another researcher receives the work. We present AIPOCH Open-Science, an open-source research workbench with portable .science session packages, and specify an exchange contract for these recorded relationships. Version and assessment targets remain attributable while receiving-workspace identifiers can be remapped, eligible contents can be omitted explicitly, and imported history remains separate from a writable fork. A controlled session tested an incorrect first version, a reviewed correction, an unreviewed revision and a planned but unexecuted step. We compared complete and partial exports with source records extracted independently of the exporter, then inspected imports into separate projects within the same installation. Both conditions retained the tested version and review associations, kept the unreviewed revision distinct from earlier assessments and represented excluded content as unavailable. A documentation-rich ordinary directory also supplied evidence for all seven static inspection questions. A separate registry case connected a table to its recorded task, execution and review; offline execution of its retained script and fixed input reproduced two saved CSV tables byte for byte. The study contributes an implemented exchange contract and a controlled examination of its preservation rules, content-selection constraints and interpretation boundaries.
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All authors Wen Ouyang, Bocheng Huang, Qianyu Yao, Fei Sun, Minying Deng, Haoyue Cheng, Wenwen Zhou, Lei Mou, Yusi Wei, Jiang Li, Kun Zhu, Lei Wang, Yi Feng, Jiaheng Dai, Ke Qian, Dan Wang, Yahui Wang, Chenyi Wang, Mingsheng Lyu, Yaqiang Zhang, Ping Zheng, Chunhua Tang, Xinge Ma, Xu Zhang, Youjia Fan, Can Lu, Zhijun Dai, Man Hu, Qiqi Xie, Beinan Liu, Yuanning Guo, Yunze Liu, Jinhong Jiang, Xianjun Fu, Mingwei Zhang, Yuanji Xu, Shilin Xia, Bowen Yang, Mingjun Yu, Jingdun Xie, Xuwei Chen, Xia Zou, Yuzhou Xue, Jianguo Zhou, Chunhao Cao, Donghao Tian, Lei Huang, Jinxiao Li, Shengrong Long, Zefeng Song, Honglin Li, Jiang Luo, Wenying Jiang, Shanshan Li, Lei Dai, Ning Jiang, Ying Cai, Chaohui Li, Chunyan Peng, Hetong Zhao, Angqing Li, Chunyan Yang, Chongyang Sun, Yong Li, Xinyu Shi, Wei Dai, Ming Peng, Chen Sang, Yuanyuan Han, Desong Qiu, Haixin Miao, Jian Xu, Yi Chen, Xiaoliang Shi, Tian Shen, Teng Hu, Shuhao Wang, Yingyong Hou, Huimei Wang

Machine Learning

Masked Diffusion for Travel Diaries Synthesis

Pascal Jutras-Dubé, Raziq Zaman, Fabian Bastin et al. (+2 more)

Because household travel surveys combine household characteristics, variable-length person rosters, vehicle rosters, and trip chains, existing synthesis pipelines split them across separate models or flatten them into cropped fixed-length vectors. Representation, rather than modeling, becomes the bottleneck: splitting severs cross-segment dependence, and cropping truncates the household-structure distribution. We instead tokenize the diary and keep the object whole: each household-day becomes one token sequence with count tokens for vehicles, persons, and trips, and distinct symbols for padded, hidden, unobserved, and inapplicable positions. We introduce DiaryBridge, a masked diffusion model that bridges the fully hidden sequence to the distribution of complete diaries by unmasking positions in any order. One trained model, conditioned on any observed subset, performs synthesis and partial-diary completion alike. Experiments on real data extracted from the 2017/2018 Regional Travel Survey of the Baltimore region show that DiaryBridge generates travel reflecting vehicle availability and employment status, recovers shared trips using other household members' observed diaries, and better reproduces aggregate demand across modes, purposes, and departure periods than the learned baselines.
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All authors Pascal Jutras-Dubé, Raziq Zaman, Fabian Bastin, Cinzia Cirillo, Ruqi Zhang

Systems and Control

A Deficiency-Based Approach for Unbalanced Three-Phase Distribution System Expansion Planning

Eli Brock, Miguel Heleno, Javad Lavaei et al. (+1 more)

Distribution planners typically characterize grid needs through power flow studies and fit upgrades to those needs by hand. More principled one-shot optimization methods either do not scale or misrepresent unbalanced three-phase networks. This paper proposes an iterative planning process that alternates between two problem classes with mature off-the-shelf solvers. An optimal power flow (OPF) problem quantifies the deficiency of a candidate portfolio by optimizing the dispatch of controllable devices and measuring thermal and voltage violations on a common scale, with an optimal value of zero exactly at adequate portfolios. The OPF multipliers give a first-order approximation of the relief each investment candidate would provide. We bound the error of this approximation and show that structural properties of the deficiency guard it against overestimation. Building on this property, a heuristic cutting-plane search uses a mixed-integer linear program to propose each next portfolio from these approximations. On six unbalanced three-phase feeders from the SMART-DS dataset, the search outperforms a linearized optimization-based benchmark, and the results agree across five variants of the deficiency.
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All authors Eli Brock, Miguel Heleno, Javad Lavaei, Somayeh Sojoudi

Computational Complexity · Cross-listed from math.CO

Block Sensitivity and Certificate Complexity of Rotation Symmetric Boolean Functions

Yuan Li and Jing Zhang

We study block sensitivity and certificate complexity of rotation symmetric Boolean functions, with particular emphasis on monomial rotation symmetric Boolean functions. We first show that every odd-degree monomial rotation symmetric Boolean function on $n$ variables satisfies $s=bs=C=n$. We then determine sensitivity, block sensitivity, and certificate complexity for every quadratic monomial rotation symmetric Boolean function. For the quadratic function $Q_{n,k}$ generated by $x_0x_k$, we prove, outside the short-orbit case, that \[ C(Q_{n,k})= \begin{cases} n,&4\mid n/\gcd(n,k),\\ n-\gcd(n,k),&4\nmid n/\gcd(n,k). \end{cases} \] Moreover, \(bs(Q_{n,k})=C(Q_{n,k})\) for every quadratic MRS function, and outside the short-orbit case, strict separation \(s<bs=C\) occurs exactly when \[ \frac{n}{\gcd(n,k)}\equiv2\pmod4. \] Finally, for the consecutive quartic family \[ R_n(x)=\bigoplus_{i\in\mathbb Z_n}x_ix_{i+1}x_{i+2}x_{i+3}, \] we determine all three measures in every residue class modulo $6$, using the sensitivity formulas and, in one residue class, finite-state results established in our companion preprint~\cite{LiZhangRSBFSensitivity}. In particular, for $r=2,3$ and $6q+r\ge5$ we obtain strict separation, \[ \boxed{s(R_{6q+r})=6q<bs(R_{6q+r})=C(R_{6q+r})=6q+1,\qquad r=2,3.} \] Thus this family gives an infinite collection of rotation symmetric Boolean functions for which sensitivity is strictly smaller than block sensitivity, while block sensitivity and certificate complexity coincide.
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Hardware Architecture

LocalTrain: Integer-Only Local Learning for Energy-Efficient CNN Training on Edge FPGAs

Shiqi Ding, Xuanyu Liang, Xinyang Liu et al. (+4 more)

Local learning reduces cross-layer training dependencies, but an efficient accelerator must still retain operands, accumulate gradients, and select quantization scales. We present an FPGA system that trains a CNN and its regional auxiliary classifiers using a shared integer matrix engine. Compact errors are expanded on chip, gradients are folded tile by tile, and historical scales permit direct output conversion. At 200 MHz, the KV260 prototype achieves 76.57% accuracy on CIFAR-10 and 91.85% on SVHN at approximately 4.20 W mean module power. Effective matrix throughput is 31.22 and 31.57 GMAC/s, respectively. Against the same integer method on Jetson Orin Nano, measured training energy is 40.4% and 42.1% lower, with $1.86\times$ and $1.82\times$ longer runtime; measurements use the respective platform power domains. Selected state checks and matching complete-run prediction counts support functional agreement with the software reference.
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All authors Shiqi Ding, Xuanyu Liang, Xinyang Liu, Boyang Li, Zhiqiang Que, Guosheng Hu, Jiayang Li

Computer Vision

Contrastive learning for video reuse detection

Tristan J.B. Cann and Arjun Biswas

The incorporation of footage from other sources has long been a concern from a copyright perspective, as such usage can harm the interests of rights holders. Recent trends in the “remixing” of social media content and the development of bespoke stock libraries for specialist causes mean that methods to identify content reuse will have a range of applications. In this paper, we develop a framework based on a Siamese network fine tuned using contrastive learning to identify potential sources for key frames in a given video. This framework makes two contributions to analysis of large video datasets. Firstly, we demonstrate its utility at reducing complex video data to a set of key visuals typically less than 10% of the size of the full set. Secondly, we show that these reduced sets allow for efficient and effective identification of component footage. We test this method over a set of videos compiled from a stock footage library and demonstrate its effectiveness for constituent video identification with average accuracy greater than 0.95.
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Cryptography and Security

SEAL-DT: An End-to-End Security Engineering and Assurance Framework for Cross-Domain Digital Twins

Iraklis Varlamis, Stelios Zindros, Christos Sardianos et al. (+3 more)

Cyber-physical systems connect physical processes, software, networks and organizations. Securing them requires a clear account of what happened, what was observed, which actions were authorized and what evidence supports each decision. Digital Twin (DT) descriptions often fail to explain how these elements stay consistent, blur the distinction between physical events and uncertain observations, or detach simulation results from their requirements and assumptions. This paper presents SEAL-DT (Security Engineering and Assurance Lifecycle for Digital Twins), a structured approach to building and evaluating secure DTs. It brings entity definitions, process models, deterministic simulations, data contracts, evidence records, automated tests and reproducible results into one connected model. The main contribution is an end-to-end DT security framework, spanning scenario definition, entity/process modelling, simulation, security decision logic, evidence handling, validation and reproducible reporting. Security decisions and simulation results thus trace back to their data, assumptions and requirements. A DT is more than a copy of system state: it states what it can observe, how accurate and current its information is, where it came from and which decisions it may support. We demonstrate SEAL-DT in five scenarios: Car2X protection, passive IoT-device detection, an industrial control surrogate, lawful acquisition in a smart home, and medical-IoT monitoring that preserves continuity of care. Each includes formal models, deterministic and negative experiments, uncertainty analyses, schema-valid fixtures, technical reports and verified manifests, testing multiple configurations under normal and adverse conditions. Results show that the framework supports consistent model construction, traceable execution and comparable internal validation, and that security findings link to observable evidence.
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All authors Iraklis Varlamis, Stelios Zindros, Christos Sardianos, Thomas Lagkas, Panagiotis Sarigiannidis, Georgios Th. Papadopoulos

Machine Learning

Towards Universal Calibration for In-Context Classification

Awik Dhar, Sunita Sarawagi and Arpit Agarwal

In-Context Learning (ICL) has received a lot of interest recently due to its ability to improve classification accuracy for new tasks without having to fine-tune the model over these tasks. However, the impact of ICL on calibration has been far less studied. Recent work has shown that ICL can have a negative impact on calibration due to biased selection of in-context examples. The existing methods for improving ICL calibration require additional task and model-specific training, detracting from the no-fine-tuning convenience of ICL. In this work, we introduce CALIBFORMER, a universal ICL calibrator for classification tasks, that generalizes to new models and tasks without further fine-tuning on these tasks. Specifically, we design a transformer-based calibrator which uses the model predictions on in-context examples to predict a confidence score for the test input. We show that the calibrator trained on three models and ten tasks, is able to generalize/calibrate ICL predictions on several new tasks and four LLMs unseen during training. Even for frontier models like Gemini, CALIBFORMER is able to obtain over 50% reduction in calibration error.
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Human-Computer Interaction

Particle Selection in Virtual Reality

Boaz Keren-Gil, James Gain, Lucia Marchetti et al. (+2 more)

Point-cloud representations of 3D data are used in many fields, from astronomical surveys of galaxies to medical imaging and laser scans of buildings. A task common to all of these is interactive selection: picking out a meaningful subset of points for closer analysis. The data is inherently 3D, yet the traditional workflow uses a 2D mouse on a 2D projection and inherits the limitations of a flat screen. This paper compares Virtual Reality (VR) and desktop environments for 3D particle selection, asking whether VR's six-degree-of-freedom hand interaction yields measurable gains in accuracy, speed, usability, workload and flow. In a counterbalanced within-subjects experiment (N = 34), participants selected 90 target galaxies from a catalogue of 16,816 galaxies, using mouse-driven selection with keyboard camera control on the desktop and a hand-held spherical brush that adds or removes particles in VR. We measured task accuracy, completion time, and three standard questionnaires: the System Usability Scale (SUS), the NASA Task Load Index (NASA-TLX) and the Flow State Scale (FSS). Accuracy was comparable (91.6% VR vs. 93.1% desktop; p = 0.858), but every other measure favoured VR. Tasks were 23% faster (p = 0.001, d = 0.93), usability was higher (SUS 84.6 vs. 54.0; d = -1.78), workload was lower (NASA-TLX 20.4 vs. 34.7; d = 0.90), and flow was higher (FSS 83.2 vs. 70.2; d = -1.23). Because the underlying problem, making a 3D selection through a 2D interface, is not specific to astronomy, we expect the pattern to carry over to point-cloud selection in other fields.
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All authors Boaz Keren-Gil, James Gain, Lucia Marchetti, Patrick Marais, Alexander Sivitilli

Artificial Intelligence

SFT Leaves a Reproducible Signature in Activation Geometry: Calibrating Post-Training Interpretability Against Run-to-Run Noise

Xupeng Chen, Chenqian Le, Jiayi Cheng et al. (+2 more)

A model diff credits post-training with whatever separates a base checkpoint from one post-trained descendant, presuming a rerun would land close by. On the sparse co-activation graph of Qwen3-1.7B it does not: two LoRA-SFT reruns sit over half as far apart as either sits from the base. We score each base-to-post contrast against the run-to-run band of its own recipe, and show that $k$ runs certify no better than $p=1/(k+1)$ under run exchangeability, however large the effect, so the band $z$-score is a descriptive scale. SFT leaves a large, reproducible signature: base $\to$ SFT displaces the graph by 17.8 pairwise-distance SDs beyond the mean distance between four independent LoRA-SFT runs, and the pre-registered estimator replicates on Llama-3.1-8B with Llama-Scope. At 29 steps each, four SFT and four DPO runs occupy disjoint displacement ranges, a gap $5.0\times$ and $5.6\times$ the spreads of the two arms, without any band. All bands use graphs built on one GPU architecture. Three other readings stop short of a verdict: DPO band counts (3/4, 1/4, 3/4 contrasts outside at 12, 29, 60 steps) show no consistent dose ordering while its displacement stays flat; light GRPO is a secondary non-detection, not an absence, and we run no equivalence test; and the graph concentration read as an RL signature is already in the zero-RL base. A pre-registered causal sweep at three edge budgets on 4,816 held-out prompts found the displaced sources load-bearing ($12\times$ a matched random control in mean, $3.5\times$ in median at 10,000 edges), but their stage-specificity interaction reversed at two budgets and the third failed its faithfulness gate, so we claim calibrated structural displacement, not a changed mechanism. Scope: one backbone, one SFT recipe and general-text probe states for the full protocol.
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All authors Xupeng Chen, Chenqian Le, Jiayi Cheng, Runhao Li, Michael Yue

Networking

MCCL: Measurement-based Collective Communication Library

Michele Ferrero, Gabriele Castellano, Paolo Medagliani et al. (+3 more)

Collective communication is a critical performance bottleneck in large-scale distributed training. Existing libraries and synthesis frameworks compute open-loop schedules once, assuming a static topology and peak link capacities. In production, however, persistent gray failures can silently degrade link throughput and latency for hours, causing pre-computed schedules to lose much of their efficiency during execution. In this paper, we present Measurement-based Collective Communication Library (MCCL), a collective communication library that closes the loop between runtime network measurement and dynamic schedule synthesis. MCCL passively measures GPU-to-GPU throughput, detects persistent degradation, re-synthesizes the affected communication-intensive schedules with warm-started synthesizers, and hot-swaps them without interrupting training. We validate the complete loop on a multi-node hardware prototype and use simulations to evaluate cluster-scale scenarios. In our experiments, MCCL incurs negligible overhead (<2%) while maintaining high measurement accuracy (<1.2% error) and low schedule hot-swap time (about 12 ms). Notably, under gray failures, MCCL reduces training time by up to 4.99x over static schedules on simulated training workloads for state-of-the-art LLMs such as Llama and DeepSeek.
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All authors Michele Ferrero, Gabriele Castellano, Paolo Medagliani, Sebastien Martin, Roberto Morabito, Massimo Gallo

Artificial Intelligence · Cross-listed from math.HO

What the Machine Invented: Mining New Mathematical Instruments from OpenAI's October 2026 Release of AI-Generated Mathematics

Ying Xie

On 6 October 2026 OpenAI published an initial 722 AI-written mathematical manuscripts, organized into 372 problem families, in the public repository github.com/openai/math. The release includes claimed solutions to many prominent open problems. We approach this collection as a treasure mine of mathematical ideas. Our aim is to extract new lemmas, constructions, inequalities and proof mechanisms introduced in the source arguments, and to distinguish them from established tools and new combinations of those tools. We call these mathematical instruments. Extraction comes first; their validity, novelty and possible reuse then become subjects for further study. The resulting descriptions also help readers understand the selected papers without having to reconstruct every argument at the outset. We examine 38 families in two groups of 17 and 21, spanning number theory, geometry, analysis, combinatorics, complexity, algebra, operator algebras, topology and mathematical physics. Each entry records the claim, extracted instruments, antecedents and caveats. Eleven cross-cutting patterns connect the entries, and a scoreboard records formalization status and dependencies. Novelty assessments are provisional and sometimes mixed. Selected source comparisons correct mathematical statements, hypotheses and counts; they do not independently establish priority or certify the underlying proofs. No Lean development was compiled or kernel-checked for this survey. We also briefly describe a subsequent application of the signed restriction lemma from family 112 to circuit lower bounds at every fixed depth.
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Robotics

Interactive Navigation Beyond Reachability: Benchmark and Agentic Framework

Delun Li, Xueyao Wei, Rui Chen et al. (+4 more)

Existing embodied navigation tasks commonly assume that traversability and object states remain fixed during an episode. Yet reaching a goal may require opening a door to access a room and then opening a container to reveal a hidden target. We formulate interactive navigation beyond reachability, unifying channel-induced changes in spatial connectivity, container-induced changes in target visibility, and their sequential dependencies. We introduce MultINav-Bench, a 3K-episode ObjectGoal benchmark in MolmoSpaces spanning channel, container, and mixed tasks, with interaction-necessity annotations covering required and unnecessary episodes. Its data generation pipeline automates task construction across multiple task types. The evaluation protocol separately measures navigation success, interaction effectiveness, and cost. We further propose MultINav-Agent, a three-stage agentic framework built on a persistent, state-conditioned interaction graph. Semantic interactive perception grounds entities and interaction states; a vision-language model selects task-relevant actions based on expected changes in connectivity and target observability; and post-interaction verification updates the graph to guide subsequent navigation, exploration, or interaction. Experiments on MultINav-Bench show that MultINav-Agent consistently outperforms adapted baselines across channel, container, and mixed tasks.
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All authors Delun Li, Xueyao Wei, Rui Chen, Yifei Wang, Junzhi Yu, Wenjie Song, Xingyu Chen

Information Theory

The Right to Be Forgotten under Correlation: Privacy-Utility Tradeoffs

Luis Massny, Rawad Bitar, Fangwei Ye et al. (+1 more)

We study privacy-preserving algorithms that redact information from a correlated dataset. The goal is to reduce the information leakage about sensitive parts. We analytically characterize the optimal tradeoff between privacy (leakage through correlation) and utility (number of redactions) for local mechanisms and binary Markovian correlations. Our result shows how to decompose the mechanism design problem into a sequence of local mechanism design problems and reveals their sequential relation. The optimal mechanism is the solution to a Finite-Horizon Deterministic Markov Decision Process.
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All authors Luis Massny, Rawad Bitar, Fangwei Ye, Salim El Rouayheb

Computers and Society

Superintelligence and the Triple Risk of an AI Race

Matthew Rendall, Max Hellrigel-Holderbaum and Leonard Dung

On many estimates, artificial general intelligence (AGI) is likely to emerge within the next few decades or even sooner. Once it does, it may quickly surpass human intelligence, possibly by a huge margin. Some believe that the country that develops AGI first could attain global dominance. This prospect risks setting off intense competition between the United States and China. Indeed, a narrative has already developed that the superpowers are locked in an AI race, with the Trump administration proclaiming its determination to 'dismantle unnecessary regulatory barriers' to winning it. We argue that this approach presents three profound dangers. The first two are familiar from past arms races: being overtaken by a rival or triggering a war. The third is new: losing control of human beings' future to agents far more intelligent than we are, which could endanger our very survival. Striving to get to AGI first risks a race to the bottom on safety practices and regulation concerning them. China does not yet appear bent on racing to superintelligence. The United States should prioritise both unilateral and co-operative efforts to improve AI safety and avoid provoking a race that all humanity could lose.
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Robotics

Declarative Reactive Robot Behaviors from Imperative Language Models

Vincent Berenz, Tasha Kim and Bernhard Schölkopf

Large Language Models (LLMs) are predominantly trained on imperative programming languages, and existing work on LLM-based robot programming reflects this: it focuses on goal-directed tasks expressed as plans. However, many robotic applications require not tasks but behaviors–open-ended, continuous patterns of sensory-motor engagement with no termination condition (e.g., greeting visitors, patrolling an area). Such behaviors are better served by declarative and reactive programming, on which LLMs have virtually no training. We investigate whether this gap can be bridged. Using Playful, a reactive scripting language, we show that an LLM-based coding agent can generate correct declarative and reactive programs for robotic behaviors. These programs are produced from under-specified natural language descriptions, given suitable context and a set of reusable programming guidelines. The guidelines encode design principles that redirect the agent from imperative defaults toward proper reactive patterns. Developed once by an expert, they generalize across robots and behaviors. Starting from only the generic robot operating system API, the agent jointly designs both the imperative component (Python modules for sensing, acting, and evaluating) and the declarative component (the reactive script that composes them into behavior logic). The declarative nature of reactive programming supports both incremental refinement through conversational turns and one-shot generation of complete behaviors.
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Computer Vision

Scaling Unified Multimodal Models to Long-Horizon Interleaved Generation

Ziqiao Peng, Yutao Cui, Wang Lin et al. (+12 more)

Unified multimodal models (UMMs) provide an interface for interleaved text-image generation, where generated visual states can directly inform subsequent reasoning and synthesis. However, existing systems are still primarily demonstrated on short trajectories or simplified visual content. We develop a large UMM natively trained on interleaved multimodal data, with strong capabilities in image synthesis, text rendering, complex composition, and multimodal planning, and study how to extend these native capabilities to substantially longer autoregressive trajectories. The main challenge is that generated images continuously accumulate in context: irrelevant or imperfect visual states interfere with future synthesis, while previously established characters and scenes must still be recovered after long intervals. We introduce a visual-memory framework that maintains persistent visual references, selectively retrieves scene-relevant historical states while preserving the complete semantic history, and uses few-step self-rollout to adapt the model to imperfect generated histories. We further introduce LongMIG-Bench for evaluating long-horizon text-to-multi-image and image-to-multi-image generation. Our model achieves high-fidelity long-horizon interleaved generation with strong character and scene consistency, demonstrating how visual memory can scale native interleaved generation to substantially longer trajectories.
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All authors Ziqiao Peng, Yutao Cui, Wang Lin, Zhong-Yu Li, Qingwen Gu, Tianhui Song, Chengyou Jia, Guozhen Zhang, Xuerui Qiu, Qixun Wang, Miles Yang, Zhao Zhong, Yonglong Tian, Hongyan Liu, Jun He

Computer Vision

Rethinking LLM-Guided Visual Question Answering: Does Deeper Knowledge Integration Really Help?

Aneerban Saha, Vijay Kumar, Sameer Madhav D. et al. (+2 more)

Knowledge-based Visual Question Answering (VQA) requires reasoning that extends beyond visible image content, demanding external commonsense and factual knowledge. While recent work leverages Large Language Models (LLMs) as knowledge sources, the question of how deeply such guidance should be integrated into a vision language architecture remains underexplored. In this work, we generate structured reasoning guidance, visual focus concepts, distractor elements and external knowledge using Llama~3.3~70B and systematically compare two integration strategies within a unified frozen ViT + FLAN-T5 framework on the OK-VQA benchmark: decision level late fusion and attention-matrix injection. Contrary to the prevailing assumption that deeper integration is superior, we find that shallow late fusion achieves the strongest results (39.4% exact match, +10.0 over the no guidance baseline), outperforming deep attention modification (31.8%) under data scarce fine-tuning. We further contribute an LLM based augmentation pipeline that expands training data more than tenfold via question paraphrasing. Our findings indicate that integration depth must be matched to the data regime.
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All authors Aneerban Saha, Vijay Kumar, Sameer Madhav D., Priyam Srivastava, Dhruv Bansal

Computation and Language

UZHCL_for_the_win at Touche: Encoder Fine-Tuning, Synthetic Augmentation, and an Underpowered Encoder-LLM Comparison

Psychias Stylianos

Online discussions on platforms like Reddit are a productive testbed for fallacy detection: short texts, broad topical range, and the full inventory of informal fallacies. The Touche 2026 lab at CLEF formalises this as a shared task on a Reddit-derived corpus that combines fallacy detection with argument-scheme classification. This paper describes our participation in all three subtasks (ST1 binary fallacy detection, ST2 eight-way fallacy classification, ST3 four-way argument-scheme classification) and both evaluation tracks. Our submitted systems are fine-tuned RoBERTa-large encoders with task-specific heads, augmented with a small batch of LLM-generated synthetic contrastive pairs; for ST2 and ST3 on the enhanced track we additionally added confidence-filtered pseudo-labelled examples from the unlabelled pool. A wider backbone comparison spanning additional encoders (DeBERTa-v3, ModernBERT) and an LLM track (four Qwen variants from 3B to 32B, zero-shot through CoT-LoRA) showed no validated benefit over RoBERTa-large at this corpus scale, with 5-fold variance wide enough that we call the comparison underpowered rather than settled. We also characterise how much of the enhanced track's lift reflects organiser-rewritten inputs carrying label information rather than genuine argument clarification. On the official Touche 2026 test set our systems ranked first on both base-track classification cells (ST2 and ST3) and second on both binary-detection (ST1) tracks, with the two enhanced-track classification cells topped by cascaded and multitask systems. We close with the rare-class limitation on ST3 and the most useful follow-ups. Code: https://github.com/psychias/fallacy_detection_clef_2026
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Logic in Computer Science

The Nonlocal Nature of Natural Deduction & the Introduction of Assumptions in Derivations

Khashayar Irani

This concise manuscript investigates the nonlocal nature of natural deduction, particularly the possibility of introducing assumptions at intermediate stages of a derivation after those stages have already been passed. By examining the structural correspondence between natural deduction and sequent calculus, we demonstrate how the structural rule of left-weakening (LW) permits the enlargement of an antecedent after its corresponding succedent has been derived, without modifying the original inference. Through a number of symmetrical derivations, we establish the proof-theoretic relationship between the introduction of assumptions in natural deduction and antecedent expansion in sequent calculus. On this basis, we formulate the principle of Nonlocal Assumption Introduction (NAI), according to which assumptions may be introduced at any intermediate stage prior to the final conclusion, provided that the corresponding structural expansion is proof-theoretically valid. We further specify the conditions governing this operation, including the preservation of inferential validity, assumption dependencies, and the scope and discharge requirements of the resulting derivation.
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