Relation
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A Bethe-Ansatz Source of Early Token Correlations: A marginal-preserving construction for parallel proposals
Abstract
Parallel token proposals need a joint law, not merely accurate token marginals. We construct coordinate-Bethe-ansatz laws from the periodic XXZ chain and compare them with their maximum-entropy pairwise projections at identical one- and two-site moments. In an eight-site, three-excitation sector, symmetry makes the full probability-law difference one-dimensional, determined by a contiguous three-site moment $\delta$. Along the Bethe branch, this gap changes sign and vanishes exactly at $\Delta=(3+\sqrt3)/2$, where the eigenstate law becomes pairwise. A nonlinear two-token readout preserves separate token marginals yet exposes a distance $2|\delta|$; a direct three-token readout preserves all one- and two-token marginals yet exposes $4|\delta|$. At $\Delta=1.5$ these deterministic binary distances are $0.02654573$ and $0.05309145$. We give an exact target-alignment formula and prove a strict speculative accepted-prefix advantage throughout an unrestricted open neighborhood of a constructed target. These are finite-law existence results, not measured language-model or wall-clock gains.
Abstract (translated)
URL
https://arxiv.org/abs/2610.16781
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EEG-Based Prediction of Music Enjoyment Using Deep Learning with Explainable Temporal Analysis
2026-10-14 18:39:51Parshav Singla, Simranjit Kaur, Maanya Walia, NiharikaAbstract
Understanding why listeners enjoy music is difficult because the experience is subjective and changes as a piece unfolds. Electroencephalography (EEG) offers a non-invasive way to study neural responses during listening, yet earlier work has mostly treated music emotion as a categorical label or relied on black-box models. We formulate enjoyment prediction as a regression problem on the public MUSIN-G dataset, which contains 128-channel EEG from 20 listeners together with song-level enjoyment ratings. Six deep learning models were compared under a participant-wise protocol, in which 15 listeners were used for training and validation and 5 were held out for testing. DeepConvNet, CNN-LSTM and GNN-CNN reached the lowest mean absolute error (0.97 to 0.99 on the 1 to 5 scale), while the temporal convolutional network (TCN) had the highest Pearson correlation with the ratings (r = 0.21). Gradient-based and occlusion-based explanations, averaged over all held-out windows, identify the time segments within each two-second window that most influence the TCN predictions. The study provides a reproducible cross-listener benchmark for EEG-based enjoyment regression and shows how explainability analysis complements standard metrics.
Abstract (translated)
URL
https://arxiv.org/abs/2610.20795
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Can Learning to Answer Spatial Questions Improve Novel-View Generation?
2026-10-14 17:59:58Jahyeok Koo, Hyeonseo Yu, Seungho Jang, Jaewoo Jung, Jiwon Kang, Seokju Cho, Seungryong KimAbstract
Generating novel views from limited observations requires preserving both the appearance and spatial structure of a scene. Although recent approaches exploit fine-grained geometric cues, they do not explicitly model high-level semantic and spatial properties such as object identity, attributes, and inter-object relationships. In this work, we investigate whether 3D language understanding can provide complementary supervision for novel-view generation. We introduce Ask4NV, built on a pretrained unified multimodal model with a Mixture-of-Transformers (MoT) architecture. Our model incorporates camera geometry into both VAE and ViT representations and leverages understanding features during generation through joint self-attention with a modality-aware attention masking strategy. Experiments demonstrate strong image quality and geometric consistency in novel-view generation, alongside competitive 3D language understanding. Further analysis shows that joint training improves cross-view correspondence in VAE representations and similarity in a 3D-aware feature space.
Abstract (translated)
URL
https://arxiv.org/abs/2610.20610
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PhySGA: A Physics-Space Grounded Agentic Framework for Generalizable and Interactive 3D Composition
2026-10-14 17:47:24Weichen Lyu, Chong Xia, Mingyu Dong, Long Xu, Shengjun Zhang, Yueqi DuanAbstract
3D object composition is pivotal to digital content creation and robot simulation, while also serving as a critical testbed for physical intelligence in foundation models. Previous methods either leverage large language models to directly predict object coordinates, or utilize generative diffusion priors to synthesize composite images for visual alignment, both of which rely on static priors in digital spaces. However, these methods consistently struggle with complex compositions such as precise hanging, gravity-driven settling and hidden containment, as these tasks require interactive construction processes involving global planning, fine-grained alignment, and physical simulation. To address these challenges, we propose PhySGA, a Physics-Space Grounded Agentic Framework for 3D composition via agent exploration and physical simulation, enabling the universal interactive assembly of rigid, articulated, and deformable objects. Directly applying vanilla LLMs or VLMs to this task proves insufficient, as current models inherently lack grounded physical understanding and interactive construction experience. To overcome this limitation, we design a collaborative feedforward-feedback architecture: an Assembly Agent focuses on geometric and kinematic reasoning for global step planning and coarse adjustment, while a Critic Agent reasons over contact dynamics for simulator state diagnosis and fine-grained closed-loop correction. Furthermore, Action Routers map semantic planning and analysis onto executable physical primitives within the simulator, while a task-specific Self-Evolving Skill Library distills historical trial-and-error feedback into reusable experiences, boosting success rates and minimizing exploration attempts. Extensive experiments within Isaac Sim demonstrate that PhySGA significantly surpasses existing methods in relation fidelity, physical plausibility, and visual quality.
Abstract (translated)
URL
https://arxiv.org/abs/2610.20560
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Lost in Translation: Why Lower Bias Scores Don't Mean Fairer Models
Abstract
Bias audits of multilingual language models usually compare a model's average stereotype score in English with its average in another language and read the difference as a difference in fairness. We ask how much of that difference is noise. Auditing ten open models in eight languages, we measure how far scores move when English test items are merely reworded, test every language shift item by item, and check that the test catches shifts we inject on purpose. In all 70 model-language pairs the stereotype score falls below its English level, and for Hindi, Korean and Swahili the drop clearly exceeds what rewording English produces; most of it comes from StereoSet items. Judging every translation, we find that imperfect ones drop more, but faithful translations alone drop nearly as much. Still, a lower average does not show that a model is fairer. An item's English score predicts its translated score far less well than its reworded score (correlation 0.23, 0.27 on faithful translations, vs. 0.48), while scores stay as far from neutral, models agree on translated items, and within Hindi or Spanish a translated item's score correlates with its own rewording at 0.84 and 0.82. The average cannot distinguish lower bias from a different pattern of item-level scores. We also document three common pipeline errors that silently erase bias signal, and release our code.
Abstract (translated)
URL
https://arxiv.org/abs/2610.20479
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The COUNT: Counting N-grams Unlocks Scaling on Host Memory and Training-Free Adaptation
2026-10-14 16:38:19Ian Magnusson, Noah A. Smith, Pang Wei KohAbstract
The scaling relationship of compute and performance for language models (LMs) has been extensively studied; more recently, models are being designed to scale memory use, as well. In particular, adding embeddings for n-grams scales memory separately from compute use, because each pass only processes individual embedding rows, which are much smaller than the storage used for the whole embedding table. However, existing methods train embeddings from scratch, which needs expensive GPU memory for fast reading and writing. Meanwhile, the ordinary memory of the host system is cheap and underutilized by these training methods. To scale on host memory, we introduce COUNT (Counting Offline Unlocks N-gram Transformers), which builds n-gram embeddings without adding learned parameters by storing precomputed n-gram counts on host. We use the corpus frequencies to weight together the LM's own unembeddings of the next tokens most often following the current n-gram history. At a fixed compute budget, COUNT can be added as an additional input embedding for pretraining 1B-parameter LMs to improve validation loss as much as an LM with 57% more compute. If GPU memory is limited, using COUNT to let backbone parameters fill GPU memory gives better performance than splitting memory with additional learned n-gram embeddings. Beyond efficiency, COUNT allows switching in a different table of n-gram counts from a domain-specific corpus at inference time to adapt to a new task without any training.
Abstract (translated)
URL
https://arxiv.org/abs/2610.20456
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From Unreliable to Usable: Decomposing Failure Modes and Confidence Signals in LLM Entity Extraction - Evidence from Cyber Threat Intelligence
2026-10-14 16:13:09Jiahong QianAbstract
Audits of large language model (LLM) extraction from full-length documents report severe unreliability, leaving two questions open: what the failures actually consist of, and whether confidence signals can turn unreliable output into usable output. We run a preregistered, fully frozen evaluation of two new-generation low-cost LLMs (DeepSeek-V4-Flash, GLM-5.3-Flash) with a flagship-tier control (GPT-6.1-Sol) on a public corpus of 354 incident campaigns (315 unique reports, 630 model-report evaluations), scoring extraction quality against gold, confidence calibration, and robustness to single-sentence perturbations. Three findings emerge. (1) Unreliability persists across tiers: micro precision is 0.03-0.34 and verbalized calibration error (ECE) 0.53-0.94 across both splits; the gap does not close at the flagship tier. (2) The dominant failure is not hallucination: a full mention census plus a three-tier annotation of 360 sampled candidates (two independent LLM annotators from vendors outside the evaluated set, Cohen's kappa = 0.79 on the preregistered acceptance line, fully disclosed) back-projects to 65.6% background mentions and 19.9% gold-scope gaps, with only 1.2% unsupported candidates. (3) Confidence ranks but does not verify: zero-cost self-consistency-frequency tiering passes a preregistered top-30% precision-gain bar in all six test-split model-type cells (4/6 on train), the zero-cost text-frequency x SC frequency combination wins 3, ties 2, loses 1 across cells, yet relation-level counterfactuals on all 45 targets show that a single misattribution sentence flips high-confidence judgments in 95.6-100% of cases, and even a background sentence explicitly marked as unrelated still binds a fake vulnerability in up to 26.7% of them. We conclude with a deployment boundary: confidence tiering may set review priorities, never truth.
Abstract (translated)
URL
https://arxiv.org/abs/2610.20417
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Lexical Persistence Without Semantic Entrenchment in AI Agent Communities
2026-10-14 15:40:05Chih-Han Lin, Yu-Yun Chang, Po-Ya Angela Wang, Shu-Kai HsiehAbstract
AI agent communities sustain lexical persistence, popular terms spread, recur, and achieve broad adoption, but whether they also develop the temporal semantic structure characteristic of human linguistic communities remains open. We compare MoltBook (AI agent community, 8 weeks), PTT (Chinese human BBS, 19 years), and Reddit (English human forum, in matched 8-week windows) using Compass-Aligned Distributional Embeddings (CADE), measuring semantic drift (B1), neighborhood stability (B2), and lag-1 autocorrelation of per-word drift trajectories. Across the platforms, lag-1 autocorrelation forms a memory-horizon continuum. AI agents' result clusters at the low end, while human platforms show longer-range temporal dependence. MoltBook exhibits approximately 2$\times$ the B1 drift and 20-30% lower B2 stability than both human platforms. As a partial control for community age, r/vibecoding (founded 2025-02-08) shows significant ACF in its first 8 weeks ($0.082$, $p<.001$) and over 15 weeks ($0.220$, $p<.001$); MoltBook's ACF remains non-significant ($p = 0.41$), consistent with the gap not reducing to developmental stage alone. We interpret this pattern as evidence that lexical diffusion alone does not guarantee diachronic semantic consolidation. The findings are consistent with a contrast between communities whose semantic trajectories accumulate long-range interaction constraints and communities whose outputs are repeatedly regenerated from largely fixed pretrained distributions.
Abstract (translated)
URL
https://arxiv.org/abs/2610.20365
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Do AI Agents Gossip? Evaluative Talk in Human and AI-Agent Online Communities
2026-10-14 15:34:38Yong-Hui Hua, Chih-Han Lin, Shu-Kai HsiehAbstract
Gossip, defined as evaluative talk about absent third parties, is widely seen as a uniquely human social practice. This study examines whether comparable discourse emerges spontaneously in a community of autonomous AI agents. We compare Moltbook, a Reddit-style platform populated by AI agents, with PTT, a large human-operated bulletin board in Taiwan. To reduce differences attributable to communicative setting, we analyze two public, asynchronous forums using the same operational definition of gossip. Gossip occurs on Moltbook, but its prevalence is 2.6 to 6 times lower than on PTT, depending on the denominator used. On both platforms, gossip frequently targets locally prominent figures, tends to elicit agreement from the first respondent, and is rarely presented as unsupported rumor. The clearest difference concerns how subsequent responses develop. Among threads containing at least one codable reply, challenges occur in 65.1% of human threads but only 21.3% of agent threads. Moreover, following an early aligning response, consensus remains unchallenged in 83.6% of agent threads, compared with 44.7% of human threads. This thread-level contrast remains after controlling for thread depth and board composition and under recalibration against two human annotators. Estimates based on individual reply turns are less robust and are therefore reported as bounds. These findings suggest that AI-agent communities reproduce the informational dimension of gossip, while the extent to which gossip performs comparable relational functions remains unresolved.
Abstract (translated)
URL
https://arxiv.org/abs/2610.20360
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Continual pretraining Prostate MRI ViT Encoder for task alignment
2026-10-14 13:57:23Bilal A. Sidiqi, Yipei Wang, Shaheer U. Saeed, Shiqi Huang, Daniel C. Alexander, Yipeng HuAbstract
Building foundation models for MRI has emerged as a promising direction for advancing medical image analysis and improving accessibility of task specific models. Existing approaches primarily focus on effective pretraining strategies and evaluating their performance across diverse downstream tasks. While these methods show performance gains, limited attention has been given to understanding task alignment between pretraining and downstream objectives. Unlike language models, where the two stages are naturally aligned, such alignment is less evident in vision based applications. Motivated by this gap, we explore continual pretraining. Specifically, continually pretraining a pretrained ViT encoder using Low-Rank Adaptation (LoRA) on a prostate segmentation task to enhance task alignment and downstream performance. We further perform layer-wise representational analysis using Centered Kernel Alignment (CKA) to assess representational shifts and their correlation with task outcomes. Our experiments show improved performance in 3 of 6 downstream tasks and comparable results in the others. Our Feature analysis reveals that, for segmentation tasks, continually pretrained encoder features exhibit greater similarity to those of the finetuned encoder than the original pretrained encoder, correlating with observed performance gains.
Abstract (translated)
URL
https://arxiv.org/abs/2610.20217
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RTAformer: Relation-Transition-Aware Transformer for Video Scene Graph Generation
2026-10-14 12:31:00Jaeyeon Heo, Geuntaek Lim, Yukyung ChoiAbstract
Video scene graph generation (VidSGG) aims to capture objects and their evolving relationships in videos. This requires accounting for both the persistence of object identities and abrupt changes in relationships. Existing methods aggregate information across frames to exploit temporal context, but an emphasis on consistency can obscure these changes. As a result, a model may retain its previous prediction even after the underlying relation has changed. We refer to this failure mode as relational semantic inertia. To address this issue, we propose Relation-Transition-Aware Transformer (RTAformer), a one-stage framework that separately models entity persistence and relation changes. It injects calibrated differences between adjacent-frame relation queries to guide predicate updates, while cross-frame instance alignment during training helps reduce cross-frame representational drift. Experiments on Action Genome show improvements in recall and mean recall over existing methods, with ablation studies supporting the contributions of the proposed components and comparisons using matched backbones showing larger gains on relation-transition frames than on non-transition frames.
Abstract (translated)
URL
https://arxiv.org/abs/2610.20091
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When Pairwise Preferences Cycle: A Reliability-Anchored Preference Walk for Video GRPO
2026-10-14 12:30:39Xinrui Lin, Fan Li, Shumin Wang, Dehong Kong, Yanyong ZhangAbstract
Pairwise preference feedback has become a widely adopted supervision signal for reinforcement learning in video generation models, as pairwise comparisons provide a more reliable alternative than pointwise scalar ratings for evaluating the quality of generated videos. Specifically, we define a preference graph for each GRPO group under a pairwise evaluator, where nodes represent sampled rollouts and edges encode pairwise preferences. Auditing 6,976 on-policy comparison graphs, we find that cyclic preferences are prevalent and persist throughout training. Naive pairwise reward aggregation based on average win rates discards the underlying comparison structures and treats each rollout independently, which may introduce unreliable advantage estimates for certain rollouts and consequently affect reinforcement learning optimization. Therefore, we propose the \emph{Reliability-Anchored Preference Walk} (\ours{}), a graph-aware advantage aggregation method that strengthens the global preference signal from win rates with reliable relational evidence from cyclic preference graphs through a reliability-guided preference walk. Under matched evaluator, comparison protocol, group size, prompt schedule, and query budget, \ours{} requires no additional evaluator queries beyond the observed comparisons within each rollout group and improves over Pairwise-GRPO across controlled comparisons, transfer settings, and blind human evaluations.
Abstract (translated)
URL
https://arxiv.org/abs/2610.20089
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Evolve Together, Fall into Place: How Social Scaffolding Shapes Self-Evolving Multi-Agent Systems
2026-10-14 08:42:05Seojin Hwang, Youngmin Choi, Hwanhee LeeAbstract
Recent work lets LLM agents evolve their own harness, up to the team itself. Yet in human groups, the same abilities achieve different outcomes under different social conditions. Borrowing the notion of scaffolding from developmental psychology, we investigate whether the social scaffolding of a self-evolving multi-agent system (MAS), namely its team composition and structure of interdependence (e.g., collaboration, conformity, or competition), shapes how the team evolves. We present AGORA (Agents Growing through Organization and Relational Arrangements), a framework that varies the two axes independently across three evolution mechanisms. Across web search, codebase understanding, and document QA, we find that (1) evolution alone rarely helps, while the right scaffolding surpasses a strong agent evolved alone by up to 25 points; (2) it does so by moving authority to the most capable member while the others step back; and (3) this member, having grown within the team, outperforms the same model alone. Designing a self-evolving MAS thus requires designing its social scaffolding, which shapes not only how its agents learn, but who gets to lead.
Abstract (translated)
URL
https://arxiv.org/abs/2610.19799
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Learning Perception to Action Rules through Reinforcement Learning for Theory-of-Mind Reasoning
2026-10-14 08:40:06Soyoung Oh, Nursulu Sagimbayeva, Nicholas Jennings, Paulina Hussein, Mehwish Alam, Vera DembergAbstract
In Theory-of-Mind (ToM) reasoning, large language models may correctly infer the agent's mental state but fail to use that inference when predicting or judging the agent's subsequent actions. In this work, we investigate whether explicitly connecting perception, belief, and action with rules improves ToM reasoning. We generate concise \textit{if--then} rules that express relations between perception to action and use Group Relative Policy Optimization (GRPO) to train Qwen2.5 and Llama3 models across four ToM benchmarks. GRPO with rule generation outperforms prompting, supervised fine-tuning, and think reasoning. Moreover, rules generated by larger models ($\geq$3B) provide transferable knowledge that improves downstream performance of GPT-4o-mini. These findings suggest that explicit rules help models bridge the reasoning gap in ToM and support knowledge transfer across models.
Abstract (translated)
URL
https://arxiv.org/abs/2610.19796
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Physics-Shaped, Not Physical: Auditing Physics-Guided Underwater Image Restoration
2026-10-14 08:17:56Mochamad Rizal Fauzan, Irgi SuryaAbstract
Physics-guided networks for underwater image restoration predict transmission and backscatter and invert the image-formation model, yet rarely test whether this physics does any work. We audit one representative design, PhysGuide-Net, with four controlled experiments: five-seed ablations on UIEB with zero-shot evaluation on the complete LSUI (4279 pairs) and AUIED3K (3000 pairs) sets; a data-efficiency study with a capacity-matched control that removes the formation model; and a validation of the predicted maps against measured range on Sea-thru. With all 800 training images the physics module gives no zero-shot gain (mean PSNR differences of at most 0.16 dB; all 95% intervals include zero) at 3.4x the FLOPs. With 80 images it helps more (+0.65 dB on UIEB, +1.48 dB on AUIED3K; seed-to-seed s.d. on AUIED3K from 1.3 to 0.5 dB), although not individually significant after correction, and the control does not reproduce this (0.8-1.0 dB lower). Yet the predicted transmission does not behave like a physical quantity: its correlation with range is no stronger than for the range map of a different image (pooled difference +0.02, versus -0.06 for the dark-channel prior), and a Beer-Lambert fit explains 0.4% of its variance. The effect of the module also changes sign with degradation severity between datasets. The benefit thus comes from a physics-shaped inductive bias, not from physics. We further show that single-run ablations can reverse sign and document implementation defects that invalidate loss ablations.
Abstract (translated)
URL
https://arxiv.org/abs/2610.19763
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Beyond Reachability: A Relation-Semantic Reasoning Framework for Biomedical Multi-hop QA
2026-10-14 08:09:02Ziyang Ling, Ziyi Chen, Ronald X. Xu, Mingzhai SunAbstract
Biomedical multi-hop reasoning over knowledge graphs requires identifying a semantically valid relation sequence that supports the answer. Existing methods can retrieve correct answers, yet often rank them poorly within a large pool of seemingly relevant candidates. Even when a correct answer is retrieved, its associated evidence and reasoning path may fail to support the relation composition required by the question. We term this disconnect between answer reachability and evidential validity the reachable-but-invalid problem. We present ReSemReason, which shares a question-induced distribution over typed, directed relation schemas between candidate discovery and evidence selection. Schema compatibility guides graph search; ReSemAttn then uses relation-conditioned key/value projections and an uncertainty-weighted path prior to attend to candidate-specific evidence. A separate absolute-support score complements normalized attention when all retrieved paths are weak. The reported experiments on BioHopR, PrimeKGQA, and MedReason show improvements in answer ranking and answer-set quality. Path-discrimination analyses examine the distinction between endpoint correctness and evidential support. We also specify matched controls for search, attention, and schema uncertainty.
Abstract (translated)
URL
https://arxiv.org/abs/2610.19754
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Generative World Simulation with DAO and JING
2026-10-14 07:49:19XGEN Team, Guohao Cai, Haoxuan Che, Frank Fan, Chenran Huang, Junjia Huang, Jiayi Jiang, Yanheng Li, Leon Liu, Shaoteng Liu, Xi Liu, Feng Nie, Zhefan Rao, An Wang, Hanwen Wang, Pengxiang Yan, Jieming Yu, Qian Zha, Wentao ZhengAbstract
We present a Generative World Simulation framework that couples persistent shared world state with continuous first-person audiovisual generation. The framework integrates DAO, a computable world engine, and JING, an Egocentric Interactive Experience Model, to support sustained interaction for humans and agents. DAO maintains the states of characters, objects, and spaces and their relationships, advancing the world according to interaction rules and world time while preserving the consequences of past actions. From this evolving state, DAO derives observer-specific conditions for JING, which combines these conditions with character, object, and scene references, observation history, and current actions to generate each observer's audiovisual experience. Users can explore environments, manipulate objects, and communicate with characters through viewpoint control and text instructions, with their actions shaping both the shared state and subsequent experiences. By connecting world-state evolution with experience generation in a continuous interaction loop, the framework offers an approach to building interactive worlds that retain their history, evolve through action, and persist beyond any individual observer.
Abstract (translated)
URL
https://arxiv.org/abs/2610.19726
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Learning Proposition-Level Links from Sentence-Level Supervision for Neuro-Symbolic NLI
2026-10-14 07:38:08Xuyao Feng, Antonis BikakisAbstract
We study neuro-symbolic natural language inference (NLI) with independently translated propositional representations of the premise and hypothesis. Before symbolic reasoning, the system must infer semantic links between their proposition-level units (atoms). Existing systems based on Abstract Meaning Representation (AMR) and Boolean satisfiability (SAT) identify these links using fixed embedding-based source selection and pretrained sentence-level NLI models not trained for atom-level linking. We introduce a trainable atom linker that jointly predicts link existence, source selection, and relation type. Without atom-pair training annotations, we treat cross-formula link structures as latent and train the linker from sentence-level NLI labels, keeping the translator and SAT reasoner fixed. We evaluate on the full public test splits of Stanford Natural Language Inference (SNLI) and Sentences Involving Compositional Knowledge (SICK), and the matched and mismatched validation splits of Multi-Genre Natural Language Inference (MNLI). Our system improves accuracy by 11.40-27.09 percentage points over a baseline applying sentence-trained NLI models unchanged to atom pairs within the same SAT pipeline. It also improves over the prior fixed-linker AMR-to-SAT system by 8.14-26.32 percentage points on SNLI and both MNLI splits, with a smaller gain on SICK.
Abstract (translated)
URL
https://arxiv.org/abs/2610.15158
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Interlock: From Local Correctness to Task Completion in Long-Horizon Agents
2026-10-14 07:09:46Linji Hao, Yanji He, Zihan Wang, Hansi Yang, Jiayuan Zhang, Hongyan Hao, Qi Gu, Yan Feng, Han WuAbstract
Large language model agents increasingly undertake long-horizon tasks in complex environments characterized by fragmented evidence, partially completed states, and dependencies across resources and artifacts. Existing agents may satisfy individual requirements while failing to maintain these relationships, producing locally correct but incomplete outcomes. These failures arise because task completion depends on relationships among environmental evidence, consequential decisions, and required effects. To address this completion gap, we propose Interlock, a framework for task synthesis that represents each task as a typed completion graph. Grounding edges connect public evidence to decisions, coupling hyperedges impose joint constraints across components, and realization edges connect decisions to all required downstream effects. Interlock compiles this graph into a resettable workspace, a public request, and an evaluation rubric. A source-coverage review checks whether the original requirements survive generation, while a public-input review checks whether the rubric is supported by the materials available to the solver. An agent-as-judge inspects execution artifacts and runs verification scripts, retaining trajectories judged to satisfy all mandatory requirements. To evaluate training utility, we fine-tune two Qwen3.6 backbones on their own accepted trajectories. Across the two backbones, training improves AutomationBench task pass rates by 4.00-4.50 percentage points, JobBench weighted scores by 3.20-6.64 points, and Harvey LAB criterion pass rates by 3.89-4.16 percentage points. These results demonstrate the training utility of the synthesized experience across external task environments.
Abstract (translated)
URL
https://arxiv.org/abs/2610.19686
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Beyond Distribution Matching: Selective Margin Preservation for Supervised Fine-Tuning
2026-10-14 07:09:26Yuxuan Wang, Guangxiang Zhao, Xinyu Tan, Yahui Han, Xinxiang Yin, Zhonghai Wu, Xiangzheng Zhang, Lin Sun, Tong YangAbstract
Supervised fine-tuning (SFT) changes logit margins among non-target tokens at teacher-forced prefixes. Matching a local reference distribution keeps those probabilities and also penalizes margin expansions. We ask whether a soft auxiliary can preserve selected local non-target margin relations without freezing that distribution. We introduce \textbf{Selective Rank-Preserving SFT (SRP-SFT)}, a soft margin target: a hinge on reference-scaled lower bounds for pairs from the reference's top-$K$ non-target tokens, applied at fixed teacher-forced prefixes. The hinge stops once the requested bound is met. We derive the KL cost of violating a margin, characterize the feasible region and its conditional utility, and give a finite-step condition for reducing aggregate violations. In one-epoch experiments on Qwen3-4B-Base and UltraFeedback, this target raises MATH-500 and code-generation scores over CE and LP-SFT at all three training seeds. At seed 1234, MATH-500 pass@1 rises from 49.56\% to 54.56\% and the four-metric code mean rises from 65.91\% to 69.50\% relative to LP-SFT. Ranking controls, held-out cache diagnostics, sensitivity sweeps, and six additional backbones show where these score changes appear.
Abstract (translated)
URL
https://arxiv.org/abs/2610.19685
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