Multi-Trace Synthesis (MTS) is proposed, which converts each symbolic program into heterogeneous reasoning traces, including executable Python scripts and CoT-augmented variants, and Perception-Augmented training, which parses diagrams into structured semantic clauses before deduction, and Consensus-Guided Multi-Trace Ensemble for efficient self-adaptive inference.
Xiaoqiang Kang, Shengen Wu, Maizhen Ning et al.· 0 citations
CAST, a critique-aware training framework that converts sparse task outcomes into action-level supervision for critique learning and policy optimization, is presented, demonstrating that critique-aware training improves the robustness of LLM agents in realistic dynamic environments.
Amir Saeidi, Zeng Zhang, Rishi Singh et al.· 0 citations
Verification-Aware Training is introduced, a plug-in framework that simulates verification at every training step and turns the resulting accept and reject patterns into supervision and improves average acceptance length and wall-clock speedup across math, code, and chat benchmarks.
Geonmo Gu, Byeongho Heo, Heejae Jun et al.· 0 citations
Manac-a-1B is the strongest model below the 7B scale, exceeding both Tucano-1b1 and Tucano-2b4 on LAMBADA-PT with large paired margins and is competitive on commonsense completion and near chance on multiple-choice reasoning, as are all small base models.
Bruno Leonardo Santos Menezes, Carlos Leonardo Souza Cardoso, Fábio Porto· 0 citations
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Over six months with four state-of-the-art LLM agents configured with web search, aggregate nowcast accuracy is broadly comparable to the institutional and professional benchmarks, with performance varying widely across individual indicators.
Xin-Yue Zhao, Ruiyi Zhang, Liqin Ye et al.· 0 citations
COGTRL is proposed, a trajectory-level reinforcement learning framework that trains LLMs to emulate cognitively grounded reasoning by jointly optimizing cognitive traces and the scientific steps produced in an interleaved manner.
Shrinidhi Kumbhar, Santosh Mashetty, Divij Handa et al.· 0 citations
AtlasNLP is introduced, a country-aware atlas of over 13,000 NLP dataset records across normalized NLP task categories, tracking both the populations represented and where datasets are produced, showing that dataset coverage is highly uneven across countries and tasks and language coverage does not imply geographic representation.
Joan Nwatu, Tsedeniya Solomon Amare, Longju Bai et al.· 0 citations
We present Arkios, a 1.04B-parameter dense transformer pretrained from scratch on 150B tokens of bilingual English-Nepali text, using a custom single-file C/CUDA training stack and a Devanagari-aware byte-level BPE tokenizer built for this project. On ARC-Easy and ARC-Challenge, Arkios exceeds three comparably sized open models (Pythia-1.4B, TinyLlama-1.1B, OLMo-1B) despite an order of magnitude fewer training tokens, likely aided by a match between our educational-web-text pretraining data and ARC's grade-school-science format rather than a general capability advantage. We report full evaluation results under standard protocols, including a correction to an earlier partial-sample estimate, and findings specific to evaluating small models in a low-resource language: the standard multiple-choice-letter prompt format used by common evaluation harnesses places this model at chance on Nepali reading comprehension, and simultaneously at chance on English in the same format, which would lead a naive benchmark run to conclude the model has no Nepali ability when in fact it does. Concretely, both languages score at chance in the letter-choice format (0.240 Nepali, 0.236 English, against a chance baseline of 0.250), while scoring the answer text directly reveals genuine, English-favoring comprehension (0.306 Nepali, 0.387 English). We describe a manifest-conditioned tool-use contract introduced during instruction tuning, where tool calls are permitted only when a tool manifest is declared in context and suppressed otherwise, and report where that contract holds and where it does not. We release both the base and instruction-tuned model weights under Apache-2.0. The training code and a small privately-sourced portion of the Nepali pretraining corpus are not released; everything needed to reproduce the reported numbers from the released weights is included here.
S. Regmi, Siddhartha Pudasaini, Chetan Phakami Pun· 0 citations
Design rules and a reproducible evaluation protocol are contributed that jointly report quality, memory, and end-to-end speed, and a foundation for automated pipeline search under realistic single-GPU constraints is provided.
This work introduces Pak3H1, the first human-validated, culturally contextualized Urdu benchmark suite for 3H alignment, comprising PakAlpaca (helpfulness), PakBeaverTails (harmlessness), and PakTruthfulQA (honesty), and underscores the necessity of human-guided localization for equitable multilingual evaluation.
Abdullah Hashmat, Usman Naseem, Agha Ali Raza· 0 citations
The Cognitively Bounded User Simulator (CBUS) is introduced, an architectural framework that explicitly models the restricted working memory of young readers through an episodic bottleneck and shows that enforcing architectural constraints is more effective for high-fidelity simulation than simply scaling raw model capabilities.
Rubric-based reinforcement learning extends RL beyond tasks with exact answers or rule-based verifiers by scoring responses against instance-specific criteria. However, this makes reward computation expensive: training requires repeated rubric judging, often with proprietary APIs or local generative LLM judges with 7B parameters or more. We study whether smaller language models can serve as efficient and reliable rubric-based judges. To make this question measurable, we construct PointRubric and RaR-Science-Static, two pointwise rubric-based evaluation datasets with instance-specific criteria and itemwise satisfaction labels. We compare three ways of extracting criterion-level judgments from small models: Generative verdicts, Yes/No Logprob margins, and Probe judges. Across both datasets, the Qwen3-1.7B Probe judge achieves the strongest criterion-level agreement among these methods, outperforming Generative and Logprob judges. Used as a GRPO reward model, it trains a policy from 0.232 to 0.643 on RaR-Science rubric score, compared with 0.594 for an 8B Generative judge baseline, while the baseline requires 10.7$\times$ more reward-judge time. Task and domain transfer experiments further suggest that Probe judges preserve criterion-level reward structure across settings.
Feng-Yu Xie, Yilun Zhao, Bingsen Chen et al.· 0 citations
A new method, called CW-Net, translates the reasoning process of an autonomous vehicle’s AI system into understandable concepts that explain its behavior.
MIT News · Artificial Intelligence· news.mit.eduAug 31, 2026
With millions of users across the world, Julia has been used to conduct cutting-edge research and to design new drugs, jet engines, heat pumps, and more.
A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.
New MIT research could lead to better materials for a fossil-fuel-free process for making the chemical that's essential to fertilizer and other products.
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