DISA decouples partition function estimation using offline importance sampling for distribution-matching LLM-RL, matching or exceeding online baselines like FlowRL on math and code benchmarks while retaining more strategy diversity.
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- background An Yang, Anfeng Li, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chang Gao, Chengen Huang, Chenxu Lv, et al. Qwen3 technical report.arXiv preprint arXiv:2505.09388, 2025. Junwei Zhang, Zhongxin Liu, Xing Hu, Xin Xia, and Shanping Li. Vulnerability detection by learning from syntax-based execution paths of code.IEEE Transactions on Software Engineering, 49(8): 4196-4212, 2023. Yaqin Zhou, Shangqing Liu, Jingkai Siow, Xiaoning Du, and Yang Liu. Devign: Effective vulner- ability id
- background Answer:from typing import Listdef median(l: List[int]) -> float: if not l:raise ValueError("The list is empty.")l.sort()n = len(l)mid = n / / 2if n % 2 == 0:return (l[mid -1] + l[mid]) / 2.0else:return float(l[mid]) Queryfrom typing import Listdef median(l: List[int]) -> float:"""Return median of elements in the list l.>>> median([3, 1, 2, 4, 5])3>>> median([-10, 4, 6, 1000, 10, 20])15.0""" Algorithm Designer Test Analyst Algorithm Designer (f)Sampled case in HumanEval. Figure 7.Case study of th
- background distinct trajectories and prevents premature path collapse. As paths diverge, inter-path interaction is gradually attenuated and eventually halted, al- lowing coherent reasoning trajectories to evolve without forced separation. To evaluate the reliability of each generated tra- jectory, we compute its perplexity based on the sequence probability: ppl(y) = exp − 1 L LX t=1 logP(y t |y <t, q) ! (7) where L denotes the trajectory length. During de- coding, paths whose perplexity exceeds a threshold
- background (pscs +nD L)(9) To analyze a concrete scenario, let's assume we can choose an sLM such that its capability is a fraction of the LLM's,i.e., ps = pL n . Using the scaling law from Assumption 3 (pM =αc β M), we can relate the costs: cs = ps α 1/β = pL nα 1/β = n−1/βcL. Substituting these into the heterogeneous cost equation 9: E[CostHeterogeneous](10) = pLcs 2 + pL −p s pL (pscs +nD L)(11) = pLn−1/βcL 2 + n−1 n pL n n−1/βcL +nD L (12) = 1 2 + n−1 n2 n−1/βpLcL + (n−1)D L (13) For the heter
- background TIR, we conduct experiments on domains beyond mathematics. Specifically, we evaluate PRUNETIR on the GPQA-diamond dataset. GPQA-diamond is the highest-quality subset of GPQA (Rein et al., 13 A Case from AIME24 Illustrating Degraded Reasoning in LLMs Problem: Define $f(x)=|| x|-\\tfrac{1}{2}|$ and $g(x)=|| x|-\\tfrac{1}{4}|$. Find the number of intersections of the graphs of \\[y=4 g(f(\\sin (2 \\pi x))) \\quad\\text{ and }\\quad x=4 g(f(\\cos (3 \\pi y))).\\] Solution: Okay, let's try to solve t
- background i . Advantages ˆAdistill i are normalized separately from those of utilization since the two rewards measure different aspects of same outcomes: J distill(θ) =J GRPO θ;{s new,1, . . . , snew,G},{ ˆAdistill 1 , . . . , ˆAdistill G } .(10) Total objective.All terms are combined in a single update: J(θ) =J util(θ) +λ 1 J rerank(θ) +λ 2 J distill(θ).(11) The utility score U(s) is updated non-parametrically via Eq. (5). The full procedure is summarized in Algorithm 1. Training hyperparameter settin
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BOOKMARKS introduces searchable bookmarks as reusable answers to storyline questions, enabling active initialization and passive synchronization for more consistent role-playing agent memory than recurrent summarization.
AWARE augments generative next-POI recommendation with LLM agents that produce user-anchored narratives capturing events, culture, and trends, delivering up to 12.4% relative gains on three real datasets.
OLIVIA treats LLM agent action selection as a contextual linear bandit over frozen hidden states and applies UCB exploration to adapt online, yielding consistent gains over static ReAct and prompt-based baselines on four benchmarks.
LLMs copy biased analyst ratings in investment decisions but a new detection method encourages independent reasoning and can improve stock return predictions beyond human levels.
CMIB uses a conditional multimodal information bottleneck to create reusable agent skills that separate verbalizable text content from predictive perceptual residuals, improving execution stability.
POPO uses bounded importance sampling on positive rollouts and a siamese policy network to achieve implicit negative gradients and stable optimization, matching or exceeding GRPO on math benchmarks such as 36.67% on AIME 2025.
Post-Reasoning boosts LLM accuracy by reversing the usual answer-after-reasoning order, delivering mean relative gains of 17.37% across 117 model-benchmark pairs with zero extra cost.
LOVER creates an unsupervised logic-regularized verifier that reaches 95% of supervised verifier performance on reasoning tasks across 10 datasets.
NoisyCausal benchmark tests LLMs on causal reasoning with structured noise, and a modular LLM-plus-causal-graph framework outperforms baselines while generalizing to Cladder.
Retrieving structured thinking traces as a corpus improves reasoning performance on AIME, LiveCodeBench, and GPQA over standard RAG or no retrieval.
VAnim creates open-domain text-to-SVG animations via sparse state updates on a persistent DOM tree, identification-first planning, and rendering-aware RL with a new 134k-example benchmark.
RSAT uses SFT on verified traces followed by GRPO with NLI faithfulness rewards to make 1-8B models produce verifiable table reasoning with cell citations, raising faithfulness 3.7x to 0.826.
OptiVerse is a new benchmark spanning neglected optimization domains that shows LLMs suffer sharp accuracy drops on hard problems due to modeling and logic errors, with a Dual-View Auditor Agent proposed to improve performance.
LLM tutors leak answers under adversarial student attacks, but a fine-tuned jailbreak agent and simple defenses can benchmark and improve robustness.
A method using predicted rectification difficulty for optimal human sample allocation in LLM-augmented surveys captures 61-79% of theoretical efficiency gains and reduces MSE by 11% on two datasets without pilot data.
GaLa uses hypergraph representations of objects and a TriView encoder with contrastive learning to improve vision-language models on procedural planning benchmarks.
Validity-calibrated reasoning distillation improves transfer of reasoning skills by modulating updates based on relative local validity of next steps instead of enforcing full trajectory imitation.
GraphBit is a DAG-based engine-orchestrated framework for agentic LLMs that achieves 67.6% accuracy with zero hallucinations on GAIA benchmarks.
o1-like models overthink easy tasks; self-training reduces compute use without accuracy loss on GSM8K, MATH500, GPQA, and AIME.
ASB is a new benchmark that tests 10 prompt injection attacks, memory poisoning, a novel Plan-of-Thought backdoor attack, and 11 defenses on LLM agents across 13 models, finding attack success rates up to 84.3% and limited defense effectiveness.
Meta Agent Search uses a meta-agent to iteratively program novel agentic systems in code, producing agents that outperform state-of-the-art hand-designed ones across coding, science, and math while transferring across domains and models.
Activation Addition steers language models by adding contrastive activation vectors from prompt pairs to control high-level properties like sentiment and toxicity at inference time without training.
COALA applies convex optimization reformulations of neural networks to direct preference optimization, claiming single-GPU training with ~18% of DPO's TFLOPs and competitive performance on multiple datasets and models up to 8B parameters.
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ARMove: Learning to Predict Human Mobility through Agentic Reasoning
ARMove is a transferable framework for human mobility prediction that combines agentic LLM reasoning, feature management, and large-small model synergy to outperform baselines on several metrics while improving interpretability and robustness.