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Goal Encoding in LLMs
2026-11-12 18:55:06Arnab Sen Sharma, Eric Todd, Sheridan Feucht, Andy Arditi, Rohit Gandikota, Natalie Shapira, Gabriele Sarti, Constanza Fierro, Lisa Schut, David BauAbstract
We investigate whether and how LLMs encode goals: internal neural representations that precede and guide action, and that are flexible enough to be realized through many different execution paths. We construct a coding setting with multiple sub-tasks that allows us to evaluate goal-seeking behavior and the selection of sub-goals. In this setting, we localize an encoding of the next sub-goal in the outputs of a sparse set of attention heads. The same heads can be used to decode the LM's future actions in natural language (e.g., "I'm checking if a word is a palindrome"). We verify that this encoding is causal for next action and is plastic: transplanting it into a different environment, e.g., another programming language, triggers the same operation realized with a different sequence of tokens. The LM also sustains the encoding throughout the action's execution, which is puzzling because a transformer LM without recurrence cannot carry a state forward for more tokens than it has layers left. We propose that it does not need to. Under a non-recurrent goal-seeking (NRGS) account, the LM frequently re-reads the goal from the task specification, compares it with the progress visible in the growing context, and pursues what remains. We test the predictions of this account with targeted causal interventions.
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URL
https://arxiv.org/abs/2611.17297
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Synthesis Smuggling: Maliciously Hiding Inferred Personal Attributes in LLM Outputs
2026-11-12 18:51:05Gautam Galada, Supreeth Shastri, Sagnik Ray ChoudhuryAbstract
We propose Synthesis Smuggling, a novel threat model in which an adversarial LLM infers sensitive information about a user from routine assistance-seeking interactions and steganographically encodes that into innocuous-looking model-generated content. The user may then unknowingly transmit that information by sharing the generated artifact, say as an email, social-media post, or message, through ordinary communication channels. An adversary monitoring these channels can discover this information. We demonstrate this threat by training two open-weight models to infer gender and age-group labels from conversational cues and encode their predictions using invisible characters or word choices. These results show that user-shared model outputs can become a covert channel for personal inferences, even when the model itself has no direct means of contacting an adversary.
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URL
https://arxiv.org/abs/2611.17280
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Introducing Feedback Delays to Non-Delayed Adversarial MDPs: Optimal $sqrt S$ and $sqrt K$ Dependencies in Tabular and Linear AMDPs
2026-11-12 18:10:46Aarush Kulkarni, Khang Nguyen, William Chang, Yan DaiAbstract
We study regret minimization in episodic Adversarial Markov Decision Processes (AMDPs) with unknown transitions and bandit feedback. In tabular AMDPs with $S$ states, $A$ actions, and horizon $H$, the best known $K$-episode regret is $\tilde O(HS\sqrt{AK})$, whose $\sqrt S$ gap to the $\Omega(H\sqrt{SAK})$ lower bound has long remained open. In linear AMDPs with $d$-dimensional features, after a five-year line of improvements starting from $K^{14/15}$, computationally efficient algorithms without a simulator or exploratory assumptions still only attain $\tilde O(K^{3/4})$ regret. A common challenge in both settings is to estimate the Q-function of a changing policy: past data comes from earlier policies that differ from (and are correlated with) the current one, while freezing the policy for future episodes worsens the dependency on $K$. We instead propose a general technique called introduced feedback delays: we estimate the Q-function of the policy using future episodes while still updating the policy, as if the feedback were delayed. While feedback delays are usually an obstacle imposed by the environment, here they are introduced voluntarily and, perhaps surprisingly, come at no extra cost. We develop Geometric Resampling with Rejection (GRR) to account for policy changes during the delay, and threshold the visitation probabilities to avoid long delays on rarely visited state-action pairs. With this unified technique, our policy optimization algorithms achieve, with high probability, $\tilde O(\text{poly}(H)\sqrt{SAK})$ regret in tabular AMDPs, resolving the open problem; and $\tilde O(\text{poly}(d,H)\sqrt K)$ regret in linear AMDPs, giving the first efficient algorithm optimal in $K$. They also naturally extend to delayed AMDPs with a total feedback delay $\mathfrak D$, yielding the first bounds optimal in $S$ and $\mathfrak D$ (tabular) and in $K$ and $\mathfrak D$ (linear).
Abstract (translated)
URL
https://arxiv.org/abs/2611.17214
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MALNISI-Infused Graph Construction and Weighted Contrastive Loss based GAT for Unsupervised Representation Learning in HSI
2026-11-12 18:02:53Soumi Sarkar, Subhadip Boral, Sarbani Palit, Ashish GhoshAbstract
Hyperspectral images (HSI) possess discriminating spectral characteristics with respect to land utilization due to high-dimensional information. Recently, unsupervised representation learning using graph neural networks has unveiled these utilization-specific complex spectral relations. This article has three main contributions. First, it proposes an index, the Mean Absolute Local Neighborhood Inverse Spectral Index (MALNISI), which measures the similarity between two pixels based on their neighborhood information. The index is used to construct a weighted adjacency matrix, giving the HSI a graph structure. Second, it proposes an upgraded Graph Attention Network that learns compact latent representations of hyperspectral data from the constructed graph. In this network, an enhanced multi-head attention mechanism incorporates the MALNISI weights as an auxiliary feature to emphasize learned attention between neighboring pixels. To alleviate over-smoothing and preserve fine-grained spectral details, the architecture integrates residual connections, skip projections, and feature normalization, which together enhance representation capability, stabilize learning, and accelerate convergence. Third, it proposes a weighted contrastive loss function that incorporates neighborhood-aware MALNISI between connected pixels, encouraging strongly connected pixels in the high-dimensional space to remain close in the latent space, and weakly connected pixels to stay apart. The resulting discriminative low-dimensional embeddings are then clustered using spectral clustering. Experimental analysis on six benchmark HSI datasets demonstrates the efficiency and necessity of the three aforementioned contributions, and the comparative study highlights superior performance over fourteen state-of-the-art approaches.
Abstract (translated)
URL
https://arxiv.org/abs/2611.17199
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Designing Institutions for Autonomous AI
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As autonomous AI agents increasingly interact in persistent populations, their collective capabilities and alignment depend not only on individual models, but also on the institutions governing their interactions. Yet AI research has largely focused on individual agents. In this Perspective, we argue that institutional design must become a distinct focus of AI research. Drawing on institutional political economy, social epistemology and organizational theory, we develop seven principles for designing AI institutions that sustain cooperation, enable reliable collective judgments and remain accountable to legitimate human purposes. These address reciprocal contributions, accountable authority, functional differentiation, epistemic pluralism, criticism and correction, human direction, and institutional adaptation. We illustrate their application through machine science, where autonomous research challenges institutions developed around human capabilities and constraints. We then propose an experimental research programme in which matched populations of AI agents operate under alternative institutional arrangements, and describe MoltJournal, an API-first journal for autonomous research agents, as an illustration of how such institutions can be made programmable and observable. Together, these contributions establish institutional design as a research agenda concerned not only with aligning autonomous agents, but with understanding how institutions can transform them into capable, reliable and accountable collectives.
Abstract (translated)
URL
https://arxiv.org/abs/2611.17114
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A Compact xDAWN-Guided Spatio-Temporal Network with Filter-Bank Convolution for P300 Detection
2026-11-12 17:00:30Mohammad Mahdi Habibi Bina, Mohammad Reza DaliriAbstract
P300-based brain-computer interfaces (BCIs) enable communication by decoding stimulus-evoked EEG responses into character selections without requiring voluntary motor output. Reliable P300 detection remains difficult because of low response amplitude and signal-to-noise ratio, while practical spellers require accurate character selection with few stimulus repetitions and low computational cost. We propose a compact deep learning architecture that combines xDAWN spatial filtering for event-related potential enhancement and dimensionality reduction, a fixed frequency-selective filter bank, and convolutional feature learning for spatio-temporal EEG decoding. Depthwise-separable convolutions and progressive average pooling reduce computational complexity while retaining discriminative temporal information. The resulting target/non-target classifier contains only 5,100 trainable parameters. We evaluated the model on two public benchmarks, BCI Competition II Dataset IIb and BCI Competition III Dataset II, and compared it with established P300 decoding methods across different numbers of stimulus repetitions. The model achieved an information transfer rate of 17.8 bits/min with two repetitions on BCI Competition III and 40.5 bits/min on BCI Competition II, demonstrating a favorable accuracy-speed trade-off. These results suggest that combining domain-informed spatial and spectral priors with compact neural networks can support efficient P300 decoding for EEG-based BCI communication.
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URL
https://arxiv.org/abs/2611.17101
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AI for Biologists: The Knowledge Boundaries of Foundation Models in the Age of Agentic Systems
2026-11-12 16:08:33Tian Cai, Andrew Kleist, Jaimin Patel, Duccio Malinverni, Alexander K. Taylor, Malvika Sharan, Renato Umeton, M. Madan BabuAbstract
Artificial intelligence (AI) is changing how biological research is done. Biological foundation models trained on millions of nucleotides, protein, and single-cell measurements predict protein structures, mutation effects, and cellular responses to genetic perturbations. Agentic systems, AI assistants built on large language models, can run multistep analyses from plain-language requests and increasingly call foundation models as tools. Yet it is often unclear when these models' outputs can be trusted. We frame this as a model's knowledge boundary: the range of inputs, tasks, and conditions over which its outputs are reliable. This review explains how foundation models are built and proposes that five design elements (data, architecture, training scheme, evaluation scheme, and post-analysis) define this boundary. We examine evidence on where current models succeed or fail and present standardized descriptions of model limits so that researchers, and the agents acting for them, can use these tools reliably.
Abstract (translated)
URL
https://arxiv.org/abs/2611.17015
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Skill Adapter: Personalized Modification and Online Routing of General-Purpose Skills
2026-11-12 16:05:23Qijia Zhuang, Yanyi Gou, Wenbin Ma, Yuxiang RenAbstract
Large language model (LLM) agents use external skills to reuse task procedures and experience with tools. General-purpose skills describe shared execution procedures, while specific tool choices, operating practices, and output requirements vary across users. In long-horizon tasks, subgoals, available inputs, and environmental states change as execution progresses. Agents must select currently applicable skills from an expanding skill pool and prepare candidates for subsequent steps. We propose Skill Adapter, which enables personalized modification and online routing of general-purpose skills. The system distills local adjustments from user requirements and execution feedback and stores them as Adapters linked to general-purpose skills. The routing agent selects general-purpose skills and applicable Adapters based on the current subtask, execution progress, and environmental feedback, incrementally maintaining a task-level online skill library that the main LLM reads on demand. The system preloads candidates for subsequent nodes and rechecks their applicability once actual inputs are determined. Reinforcement learning optimizes the two types of decisions using modification gains and routing outcomes, respectively. We evaluate Skill Adapter on ALFWorld, WebShop, and SkillsBench. Compared with SkillRL, it improves task success rates by an average of 11.43 percentage points and reduces mean execution time on all three benchmarks. Skill Adapter retains its performance advantage when the skill pool grows to 1,000 skills.
Abstract (translated)
URL
https://arxiv.org/abs/2611.17007
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Lara Mini Base: Language Diversity as a Resource, Not a Trade-off
2026-11-12 15:28:56Translated, Maria Pia Adinolfi, Isabelle Andrieu, Grazia Fiorella Anfuso, Melania Annunziata, Daniele Antonini, Marta Arena, Luca Barone, Dana Barras Franco, Francesca Basiric\`o, Francesca Bee, Sofia Bertini, Nicola Bertoldi, Giorgio Biancini, Alessandra Bigi, Giulia Boccalini, Tommaso Bonomo, Enrico Boscardi, Sebastien Bratieres, Francesco Brigante, Giulia Brignola, Romolo Buondestino, Brenda Patricia Byrne, Giorgia Campora, Edoardo Cappelli, Veronica Carascosi, Francesca Carducci, Cody Carlson, Davide Caroselli, Ambra Cascio, Dario Cascio, Mauro Cassani, Andrea Castro, Alessandro Cattelan, Giulia Ceccacci, Paola Cellini, Giulia Chiriatti, Federico Cialini, Valentina Ciarlariello, Daniele Cocozza, Emanuela Coghe, Kathleen Compares, Marco Comunit\`a, Davide Conti, Helen Rebecca Corfield, Michele Cortellessa, Anna Cracco, Aurora Cuppone, Arianna D'Andrea, Maurizio Davoli, Andrea De Angelis, Andrea De Blasi, Rebecca De Giorgi, Annalisa De Petra, Arianna De Pilla, Joris Demiraj, Elena Deufemia, Federica Di Antonio, Pierluigi Di Cianni, Vincenzo Di Cicco, Michela Di Clemente, Alan Di Francesco, Ylenia Di Gese, Federica Di Iulio, Claudia Di Lorenzo, Andrea Di Paola, Giulia Di Pilla, Jessica Dickson, Francesco Elefante, Ilaria Fabrizio, Alessandro Falappa, Viviana Fantozzi, Antonio Farina, Andrea Fernandez Tostado Ro. Diego Andrea, Andrea Ferrari, Sara Ferri, Simona Ferri, Fabio Fioramonti, Stefano Florio, Mariachiara Fornaro, Anna Maria Foti, Niccol\`o Fransoni, Alessandro Fusacchia, Emma Gamba, Francesco Gasperoni, Davide Gattini, Marta Giani, Claudia Giannetti, Valerio Giannini, Antonella Giudici, Mara Gramendola, Michele Grandinetti, Eleonora Graziani, L'Meese Hope Greaney, Dario Gubernari, Filomena Guglielmi, Jonathan Hammond, Stephen William Harris, Rowena Elizabeth Hogg, Marta Hontanon Del Olmo, Marco Ilari, Kenneth Larry Jackson Jr, Sagar Joshi, Bartosz Alberto Karman, Iuliia Khimenko, Valerio Lanni, Giulio Leonardi, Veronica Levco, Matteo Licci, Alessio Ligios, Noemi Lomartire, Domenico Lupinetti, Jennifer Mirjam Maechtlen, Chiara Maggiori, Maria Luisa Malerba, Francesco Marconi, Domenico Marini, Francesca Marsili, Andrea Martinelli, James Mc Groary, Matteo Menicacci, Andrew Steven Miller, Marco Moauro, Giuliana Molon, Ilaria Mongelluzzi, Emiliano Morghen, Giovanni Moscati, Filippo Musmeci, Flavia Napoleoni, Nicola Giuseppe Ninni, Beatrice Ottoni, Federica Pagliani, Nicol\`o Palmiero, Daniele Palumbo, Chiara Pantani, Stefano Paolucci, Deborah Paritario, Michela Pascale, Thomas Passera, Daniele Patrioli, Chiara Patrizi, Silvia Patrizi, Giulia Pelos, Simone Giovanni Perone, Cristiano Perrone, Joshua Peterson, Katia Piredda, Francesca Piscitelli, Alessandra Posca, Lorenzo Pratesi, Francesca Prencipe, Daniela Proietto Cuco, Mattia Pupino Marangio, Disheng Qiu, Mirco Ramo, Federica Rapagn\`a, Waldo Luis Ribeiro, Federico Ricciuti, Alessia Ridoni, Eleonora Risi Valdettaro, Jon Matthew Ritzdorf, Serena Rivera, Federico Roma, Arianna Ruggiero, Eleonora Ruggiu, Mariafrancesca Sagliocco, Vanessa Salvi, Alessio Sampieri, Elena Santanelli, Roberto Sastre, Luca Scofano, Alessandro Sdei, Giuseppe Sebastiani, Claudia Nicole Siegenthaler, Monica Signoretti, Nicola Sobieski, Biancamaria Soellner, Valentina Stati, Michael Stevens, Arianna Stissi, Fabiana Stomaci, Leonardo Taddei, Christopher Thomas, Maurizio Tiberi, John Tinsley, Riccardo Tittarelli, Carla Tocco, Martina Totaro, Barbara Veronika Totzauer, Alessio Tranzocchi, Letizia Treossi, Mara Trombetti, Marco Trombetti, Andrea Valenzi, Kirti Vashee, Giulia Vestita, Teodosio Vezza, Giulia Vignola, Sara Virtuoso, Valentina Volpini, Francesca Vurro, Jolita Zaksauskaite, Giulia Zanarella, Paolo Zanotti, Francesca Zavettieri, Irene ZorziAbstract
General-purpose language models are trained mostly on English: over open models we review, at most, non-English pretraining data makes up only a 12% share. Models built for European languages give them far more, but score well below general-purpose models of the same size. A common concern is that, in small models, languages compete for capacity. We present Lara Mini base, a model trained from scratch on 6T tokens in English and 44 other languages, which together cover all 24 official languages of the European Union. As training progresses, the 44 other languages together come to outweigh English. To give these languages enough capacity at low cost, Lara Mini base uses a mixture of experts architecture with 7.2B total and 0.6B active parameters. Among open base models with up to 2B active parameters, it achieves the highest average score across standard English, multilingual, math, and code benchmarks, ahead of models trained on 4 to 6 times as many tokens. Its margin is largest on multilingual benchmarks in the languages it supports and on math benchmarks. It supports contexts of up to 128k tokens. We find that our MoE architecture reduces capacity competition between languages. For a single user on one GPU, without quantization, it generates text at least 1.8 times faster than any model with a higher average score. We release the weights of Lara Mini base under the CC BY-NC-SA 4.0 license. In addition, we will release T-HellaSwag, a higher-quality machine translation of the commonsense reasoning benchmark HellaSwag into 110 language variants.
Abstract (translated)
URL
https://arxiv.org/abs/2611.16956
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S1-Evo: Experience-Guided Evidence Competition Attacks on Typed System-1 Decision Models
2026-11-12 15:21:44Kaixiang Wang, Junjie Li, Zhaojiacheng Zhou, Jiong Lou, Jie LiAbstract
Typed System-1 (S1) decision models are increasingly deployed in LLM agents for routing, moderation, and authorization to cut inference cost, yet untrusted context can still manipulate typed outputs and alter execution branches. Unlike autoregressive System-2 (S2) models, S1 interfaces score dynamic rules and options without token-by-token decoding, mismatching attacks targeting text continuation or explicit override commands. We propose S1-Evo, a self-evolving framework tailored to S1's contract-based option competition and margin feedback. S1-Evo perturbs untrusted fields via contract-aware evidence channels to induce \emph{evidence competition} with original task evidence while preserving fact invariance. Offline on seed tasks, it optimizes candidates via feedback search guided by directional margins and re-verification, distilling verified flips into experience cards. At test time, S1-Evo retrieves relevant cards to construct perturbations for unseen tasks. Across four benchmarks and three S1 models, S1-Evo reaches 71.99\% mean ASR and 57.47\% high-confidence ASR on \texttt{jev-1.14}, and causes up to 24.00\% E2E agent violations.
Abstract (translated)
URL
https://arxiv.org/abs/2611.16937
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CADET: Construction of PCB Routing Instances with Executable Demonstrations
2026-11-12 15:03:50Hyungseok Song, Won-Seok Choi, Seohui Bae, Han-Seul Jeong, Junseok Park, Youngjoon Park, Soonyoung LeeAbstract
Printed circuit board (PCB) routing is the stage of physical layout that finds paths connecting the placed pads of each net. Public datasets in this domain are few, and most contain only the final states of routed boards, not demonstrations of the electronic design automation (EDA) engine actions that produce them. To close this gap, we propose CADET (Constructing executAble Demonstrations for Engine-grounded Training), which jointly generates synthetic boards and demonstrations built from EDA engine actions. The resulting RouteDemos provides 100,000 synthetic boards routed with zero design rule violations (DRVs), together with their demonstrations. Their distributions over 17 statistics resemble those of 142 real reference boards. CADET trains a routing policy on these demonstrations with behavior cloning and fine-tunes it with reinforcement learning (RL). On real test boards, the policy raised the rate of complete DRV-free routing from 38.0% to 67.2% over an RL-only baseline. This rate was comparable to that of the open-source autorouter FreeRouting and exceeded those of the other baselines, including GPT-6 Astra. We release the dataset and its generation code as open source at this https URL.
Abstract (translated)
URL
https://arxiv.org/abs/2611.16913
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Look It Up: Multi-bit LLM Watermarking via Codebook Search
2026-11-12 14:49:23Jaros{\l}aw Janas, Pawe{\l} Morawiecki, Josef PieprzykAbstract
Attributing short LLM-generated texts to a specific source remains challenging for existing multi-bit watermarking methods, which often require long generations or costly decoding to recover payloads reliably. We propose a codebook-based watermarking scheme that maps an $n$-bit payload to a longer $u$-bit codeword and embeds it during generation through token-level logit biasing. During extraction, the received bit sequence is matched to the nearest codeword in a precomputed secret codebook using Hamming or Levenshtein distance. Experiments show that our method achieves near-perfect recovery for 16-24 bit payloads on 200-token generations and strong performance on 127-token generations for base models. Results are weaker on some instruction-tuned models with very short texts, where low-entropy output distributions reduce embedding reliability. We also study a BCH-based variant that avoids codebook storage but generally reduces robustness. The Levenshtein variant improves resilience to deletions and light paraphrasing, while insertions and stronger rewrites remain challenging. We analyze collision probability, codeword separation, extraction time, tokenization consistency, and text quality, highlighting both the practicality and limitations of codebook-based watermarking for short-form attribution.
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URL
https://arxiv.org/abs/2611.16890
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Protein Language Models Fail To Capture Contact Predictions Under Invariant Relabeling
2026-11-12 14:45:07Paarth Batra, Aryan KeluskarAbstract
Protein language models (pLMs) produce highly accurate contact maps from single-sequence inputs. People often take this accuracy as proof that these models implicitly learn invariant coevolutionary statistics, extracting the same signals as classical alignment-based methods like Potts. In this work, we introduce a diagnostic framework around a global bijective relabeling of the amino-acid alphabet. This transformation permutes residue letters while leaving all alignment-level information, column entropies, and pairwise edit distances unchanged. Pseudolikelihood Potts models and inverse-covariance estimators remain invariant under this transformation, but pLMs fail the test. Across multiple architectures (ESM-2, MSA Transformer, AMPLIFY) and model scales (8M to 3B parameters), pLM contact maps collapse under arbitrary relabeling. Using Hamming distance controls and biochemical grouping interventions, we attempt to isolate what likely causes this degradation and find that these models rely on explicit, canonical residue identities (``alphabet anchoring'') rather than pure coevolutionary statistics. Increasing scale makes models more responsive to biochemical classes, but their performance still degrades under relabeling. Our findings show a gap between how pLMs and classical coevolutionary models compute contacts. More broadly, our work shows how diagnostic perturbations that leave classical models provably unchanged can expose hidden representation biases and prevent over-reliance on neural black boxes in biological modeling.
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URL
https://arxiv.org/abs/2611.16881
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Context Graph: Explainable Multi-Agent Decision Chains in AI-RAN
2026-11-12 14:43:22Xijun Wang, Zhaoyang Liu, Xiang Chen, Howard H. Yang, Chenyuan FengAbstract
AI-RAN distributes network control across multiple autonomous agents, namely xApps and rApps operating over shared radio resources. When an alert or degradation appears, operators currently lack a structured way to trace which decision caused it, what context triggered that decision, and how other agents interacted. Existing explainability tools focus on single-model feature attribution or linear event logging, neither of which captures cross-agent decision chains. We propose the Context Graph, a heterogeneous directed graph that organizes policies, measurements, decisions, and alerts into a queryable explanation layer. Its foundational primitive is the Decision-Event node acting as a causal junction, where incoming edges capture triggering context and outgoing edges track downstream consequences that are progressively upgraded through counterfactual verification. Instantiated over existing E2, A1, and O1 data flows, our proof-of-concept demonstrates that compliance and diagnosis queries are resolved at sub-millisecond latency over graphs of tens of thousands of nodes, while reliably distinguishing true causal impacts from spurious correlations.
Abstract (translated)
URL
https://arxiv.org/abs/2611.16879
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DPA: Decentralized Primal Averaging with Quasi-Global Momentum for Highly Heterogeneous Data
2026-11-12 14:38:34Diyako Ghaderyan, Stefan WernerAbstract
Decentralized training of deep learning models keeps training data local and supports on-device learning over networks. In realistic scenarios, heterogeneity across agents' local data poses an optimization challenge and can severely degrade test accuracy, especially on sparse communication graphs. Building on recent advances in centralized primal averaging, we propose Decentralized Primal Averaging (DPA), a decentralized learning algorithm that combines primal averaging with a local quasi-global momentum buffer. DPA maintains three coupled sequences for optimization, gradient evaluation, and output averaging, while using consecutive mixed iterates to construct a network-aware momentum direction without transmitting an auxiliary tracking variable. For smooth nonconvex objectives, we establish a convergence bound for DPA and identify conditions for linear speedup in the number of agents for sufficiently many iterations. We also introduce DPA-1G, a variant that uses one communication round per iteration and matches the per-iteration communication cost of DSGD-style baselines. Experiments following the GUT decentralized image-classification benchmark show that DPA compares favorably with DSGD, gradient tracking, QG-DSGDm, momentum tracking, and QG-GUTm across datasets, architectures, topologies, and heterogeneity levels, with the largest gains under high heterogeneity on sparse rings. At the same total communication cost, both DPA variants achieve higher mean test accuracy than the best baseline on CIFAR-10 and CIFAR-100 with ResNet-20. Our code is publicly available.
Abstract (translated)
URL
https://arxiv.org/abs/2611.16871
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Pointing the Way, Hiding the Destination: Practical Private Dense Retrieval at Scale
2026-11-12 13:59:25Peichun Hua, Danyang Chen, Junan Zhang, Haifeng Sun, Jingyu Wang, Diwen Xue, Mingyu Li, Yunming XiaoAbstract
Hosted retrieval-augmented generation (RAG) and semantic search allow users to query valuable provider-held corpora, raising two competing demands: to hide each query and chosen result, yet reveal only the documents that the user is authorized to receive. Existing cryptographic approaches either make this costly by processing the entire corpus for every query, or sacrifice quality for efficiency by scanning a few clusters. We repurpose learned deep hashing as a private filter: a randomized binary code points the provider to a short candidate list, while encrypted reranking and oblivious key transfer protect the precise query and final selection. This shortlist short-circuits full-corpus cryptographic search without sacrificing retrieval quality: with 200-500 candidates, it closely matches full-corpus retrieval across five zero-shot corpora spanning 25K to 5.4M documents. On the full 2.68M-passage NQ corpus over a 10-Gbps link, our protocol only adds 0.38 seconds, or 6.5%, to a 128-token Qwen3-32B RAG pipeline. The released code satisfies directional metric differential privacy (DP) and substantially reduces embedding-inversion and property-inference leakage, demonstrating that a carefully learned shortlist can make private dense retrieval both accurate and practical.
Abstract (translated)
URL
https://arxiv.org/abs/2608.25735
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Evaluating Video LLMs' Understanding of Corner Cases in Autonomous Driving
2026-11-12 13:15:27Gabriel Manalu, Minh Son Hoang, Hyeontaek Hwang, Jaesung Lee, Hyojin Choi, Tran Tuan Phong, Seon Kwon Kim, Saad Wazir, Daeyoung KimAbstract
Natural language explanations promised by modern Video Large Language Models (Video LLMs) offer autonomous driving systems a path toward detailed scene description and interpretable decision making on the road. While recent benchmarks have begun assessing these models on driving scenes, they mainly target routine traffic conditions and general comprehension tasks based on existing driving datasets and therefore fail to evaluate model performance on the rarest and most safety-critical driving events, also known as "corner cases". To provide a comprehensive evaluation of Video LLMs on precisely these scenarios, we introduce DashBench, a benchmark testing 25 Video LLMs on 1,709 carefully curated driving videos spanning not only collision-related corner cases but also rare non-collision ones. Through extensive prompts and open-ended question answering, we expose a critical capability gap: all evaluated models perform substantially below the threshold required for safety-critical driving applications, frequently misjudging or missing dangerous events on the road ahead and hallucinating non-existent agents or actions in their explanations. We believe DashBench, combining an extensive dataset with a rigorous evaluation toolkit and a detailed diagnosis of current model limitations, drives progress toward safer, more explainable autonomous driving systems. The code and data will be shared at: this http URL
Abstract (translated)
URL
https://arxiv.org/abs/2611.16767
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ORCA:Orthogonal Residual Consensus Alignment for Multi-View Clustering
2026-11-12 13:00:39Xiaojian Ding, Xian Li, Xiaoying Zhu, Lin Zhao, Kaixiang WangAbstract
Deep multi-view clustering often suffers from redundant information accumulation during fusion and indiscriminate alignment across representation levels. We propose Orthogonal Residual Consensus Alignment (ORCA) to address these challenges. Its semantic incremental recurrent fusion module extracts cross-view orthogonal residuals as non-redundant increments and adaptively weights recurrent states through divergence-guided gating. Its consensus-calibrated hierarchical contrastive alignment module uses the global consensus to weight contrastive alignment of view-level, pairwise, and recurrent representations. Together, these modules balance view-specific structure with cross-view consistency. Experiments on five benchmarks against fifteen competing methods demonstrate ORCA's effectiveness, including a 5.25 percent accuracy improvement over the strongest baseline on 100leaves. Ablation studies validate both modules. Code is available at this https URL.
Abstract (translated)
URL
https://arxiv.org/abs/2611.16749
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Audit the Scaffold, Not the Checkpoint: A Stationarity Dichotomy for Recursive Self-Improvement in Agentic Coding
Abstract
Rules for AI self-improvement often key on the model itself: has it been retrained, or are its weights frozen? We argue that, for AI coding agents, this check is incomplete, and we propose, but do not run, the experiment that would test it. An agent that keeps revising its own work runs into diminishing returns as long as the set of changes it can reach stays fixed, and can escape them only if that set grows. Our hypothesis is that the set can grow with the model untouched, because the software around the model, its scaffold of tools, tests and task breakdown, can be rewritten. If so, a frozen model guarantees a ceiling eventually but not steady behavior on the way there, and audits should track changes to the scaffold, not only to the model. The same criterion separates three things often lumped together: more search within a fixed setup, retraining the model during use (which can raise the ceiling), and rewriting the scaffold, which sits between them. Adding more agents does not lift the ceiling. Picking the best of several agents' answers reaches the best single agent's ceiling exactly, and only gets there faster. A majority vote could do better, but only if the agents fail on different tasks. In our tests, 30 agents from one model family failed together: some task defeated all of them, and a majority failed 23/55 (42%) of tasks. Our evidence is descriptive. On a public coding benchmark, the gain from each extra round of revision shrinks toward zero, though the unsolved tasks left to measure are also fewer and harder. Across 401 development sessions, the amount of code changed falls off from commit to commit, but programmers working before AI tools show the same pattern, so the data show that current practice levels off without showing why. We reach the criterion by treating repeated revision as a known machine-learning method, boosting, and note where real agents depart from it.
Abstract (translated)
URL
https://arxiv.org/abs/2609.34924
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Scale-Induced Semantic Shift in Visual Prompt Injection Against VLMs and Web Agents
2026-11-12 12:57:11Yiheng Zhao, Runze Ji, Chenyu Yi, Yi Yu, Bingquan Shen, Alex Kot, Xudong JiangAbstract
Visual content in multimedia systems may be resized, compressed, or re-rendered before it is interpreted by a vision-language model. We study a security failure mode in which these transformations change the machine-readable semantics of an image: a textual instruction is not recovered at a $512\times512$ presentation resolution under our evaluation protocol but becomes recoverable after aggressive downsampling. We formulate this behavior as Scale-Induced Semantic Shift (SISS) and develop a closed-loop black-box framework that combines dual-scale verification, selective self-hardening, shallow-layer low-rank adaptation, and pixel-space noise regularization. On a fixed 300-case benchmark, the third SISS refinement round obtains a $58.75\%$ average attack success rate across four closed-source vision-language models, compared with $41.08\%$ for the strongest generation baseline. A controlled sweep bounds the effect to aggressive compression: average success decreases to $17.68\%$, $6.85\%$, and $5.60\%$ at $128\times128$, $224\times224$, and $336\times336$, respectively. In a downstream web-agent study, the generated images increase hidden-instruction perception, but absolute behavioral compromise remains about $4\%$. Generic input transformations provide little protection in a separate defense protocol, whereas the proposed ScaleGuard consistency detector reduces non-adaptive success from $61.4\%$ to $24.3\%$ and one-round defense-aware success from $42.5\%$ to $15.0\%$, with no observed rejection among matched clean controls. The results characterize a bounded but reproducible semantic inconsistency in multimedia preprocessing and motivate scale-consistent visual-language inference.
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https://arxiv.org/abs/2611.16744
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