cs.CL · 2026-08-08 · No. 78
Computation and Language, 2026-08-08.
15 new papers in cs.CL. Titles, authors,
abstracts. Links to arXiv. Want this in your inbox every morning? Subscribe →
01 — The papers
15 entries-
01
Learning When to Trust via Selective Context Preference Optimization
Xian Sun, Wei Chow, Yingshuo Wang, Junhao Liu, Wei Gao, Qing Wu, Lingdong Kong
cs.CL · cs.AI · cs.LG
Language models increasingly condition their answers on external signals, and a single misleading one can turn a correct answer wrong. The obvious remedy, training models to resist such signals, hides a failure mode: a model that ignores all context looks robust yet is useless when the context is worth trusting. We recast the problem as selective trust and introduce MIST, a human-annotated benchmark that renders each reasoning item under four...
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02
Benchmarking the Benchmarks: Evaluating Benchmarks for Conversational Agents
Noam Koren, Roy Bar-Haim, Abigail Goldsteen
cs.CL · cs.AI
Task-oriented conversational agents are evaluated using curated or automatically generated benchmarks, yet benchmark quality is rarely assessed. Poor benchmarks may contain inconsistent tasks, simplistic scenarios, or limited policy coverage, leading to unreliable evaluations. We introduce a reference-free framework that uses LLM judges to assess benchmark consistency, complexity, and policy coverage, while providing actionable diagnostics of...
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03
Beyond Sequence Order: Syntax-Informed Positional Embeddings for Transformers
Haris Riaz, Hyungji Kim, Mihai Surdeanu
cs.CL · cs.AI
Positional embeddings (PE) in Transformers encode token distance and order but are largely agnostic to \textit{syntactic structure}. We introduce \textbf{S}yntax-\textbf{i}nformed \textbf{P}ositional \textbf{E}mbeddings (\textbf{SiPE}), which learns a lightweight syntactic prior from dependency parses during pretraining and injects it across all three dominant PE families (absolute, relative, rotary), for both encoders and decoders, leaving...
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04
FormBharo: Designing and Evaluating a Voice Agent for Conversational Form Filling in Rural India
Aman Dalmia, Sanskriti Midha, Jigar Doshi
cs.CL · cs.AI · cs.HC
In India, almost every social benefit starts with a form, yet the people who need these benefits most are often unable to read or write. Reaching them requires a spoken conversation. Today that work falls to frontline health workers who enroll beneficiaries one at a time, a poor use of stretched capacity. We built FormBharo ("fill the form" in Hindi), a voice agent that fills a structured form over a phone call under tight latency and cost...
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05
MACRO: Markov Chain Routing of Transformer Layers
Paweł Batorski, Abtin Pourhadi, Akylgali Aitaza, Przemysław Spurek, Paul Swoboda
cs.CL · cs.AI
Standard Large Language Models (LLMs) execute layers sequentially. Dynamic layer routing, i.e. search for a different execution path through layers involving layer repetitions, skips and other moves, can improve performance. Existing routing approaches often require updating model weights, running expensive search loops per test instance, or demand ground-truth labels during inference. In this work, we propose Markov Chain Routing of...
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06
MameLoshnLM: Yiddish Language Model and Evaluation Benchmark
Uri Katz, Omer Goldman, Tomasz Limisiewicz, Reut Tsarfaty, Noah A. Smith
cs.CL · cs.AI
We present MameLoshnLM, the first open-source 8B-parameter language model built specifically for Yiddish. Despite Yiddish's rich textual tradition, its limited digital presence and the scarcity of reliable evaluation resources have constrained progress in Yiddish language modeling. Existing multilingual corpora and benchmarks are often poor proxies for the language, containing substantial amounts of noisy, machine-translated, and...
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07
Hierarchical Latent Prediction for Language Models
Chang Shi, Tim Pearce, Manan Tomar, Siddhartha Sen, John Langford
cs.CL · cs.AI
While standard Next-Token Prediction (NTP) lays the foundation of language model pre- training, its teacher-forced training paradigm may not be optimal for long-horizon reasoning and planning. Recent works such as Multi-Token Prediction (MTP) and Next-Latent prediction (NextLat) try to mitigate the problem through predicting multiple future tokens and self-supervised prediction in the latent space. However, those auxiliary objectives either...
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08
On-Policy Delta Distillation for Multilingual Math Reasoning
Byeongho Heo, Jaehui Hwang, Sangdoo Yun, Dongyoon Han
cs.CL · cs.LG
On-Policy Distillation (OPD) is emerging as a promising alternative to reinforcement learning for LLM post-training, yet its effectiveness in multilingual settings remains underexplored. We study OPD and its advanced variant, On-Policy Delta Distillation (OPD$^2$), for mathematical reasoning in English, Korean, and Japanese. OPD$^2$ improves OPD by using the probability gap between a post-trained teacher and its base model as the learning...
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09
Task-Conditional Flow Matching for Balanced Multilingual Text Embedding Adaptation
Tirth Bhatt, Naren Kumar S, Mayank Singh
cs.CL · cs.AI
Multilingual text embedding models are commonly adapted using a single training objective across diverse tasks, despite different tasks requiring fundamentally different optimization strategies. We introduce Task-Conditional Flow Matching (TCFM), a multilingual embedding adaptation framework that selectively applies Flow Matching to translation tasks while optimizing retrieval, classification, and pair-classification tasks with objectives...
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10
Once a Response, Always a Response: Detecting LLM-generated Text via Latent Prompt Restoration
Hongrui Bao, Yubing Ren, Yanan Cao, Jinhan You, Fang Fang, Shi Wang
cs.CL · cs.AI
Large language models (LLMs) can generate fluent and convincing text at scale, creating growing risks for misinformation dissemination, educational misuse, and platform governance. These concerns make robust detection of machine-generated text increasingly necessary. Recent zero-shot detectors mainly exploit probability-based statistical discrepancies, but they do not explicitly account for the training process of LLMs, which leaves a...
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11
Sparse Mutual Information Graph Averaging for Improving Random Indexing Embeddings
Sriram Loganathan, Gokul Anand, Aung Bo Bo, Yourui Shao, William B. Andreopoulos
cs.CL · cs.LG
Sparse word embedding pipelines can avoid dense co-occurrence matrix materialization, dense factorization, and gradient training while still relying on sparse global corpus statistics. This paper studies Random Indexing (RI) vectors refined by weighted averaging on a sparse Positive Pointwise Mutual Information (PPMI) graph. On a fairytales corpus, the covered semantic analogy set consists of 272 Google family- category questions. On this...
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12
Answer First, Reason Later: Commitment Order in Diffusion LLMs
Jewon Yeom, Jaewon Sok, Seonghyeon Park, Jeongjae Park, Hwiyeong Lee, Taesup Kim
cs.CL · cs.AI
Masked diffusion language models (dLLMs) can commit tokens in any order -- a freedom marketed as their core advantage over autoregressive decoding. We show that on reasoning tasks this freedom is instead the axis of failure. Logging every commitment during decoding of LLaDA-8B on GSM8K, we find that unconstrained (pure) decoding commits the final answer at 15-24% of the trajectory while half the reasoning region is still masked, and collapses...
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13
Relay, Don't Route: Adaptive Population Handoff for Cost-Efficient LLM-Driven Evolution
Sichun Luo, Yi Huang, Guanzhi Deng, Haibo Wang, Haochen Luo, Lei Li, Zefa Hu, Junlan Feng, Qi Liu
cs.CL · cs.AI · cs.NE
Large language model (LLM)-driven evolution has shown promise for program search and algorithm discovery, but relying on strong models throughout long evolutionary runs is costly. A natural alternative is to combine cheap and strong models under a fixed inference budget. However, existing approaches typically allocate models at the level of individual queries or mutation steps, overlooking that evolutionary search is \textit{stateful}: each...
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14
FOCUS: Decoupling Expert Personas in LLMs to Enhance Domain Expert Capabilities
Guanyu Wang, Zidi Zhang, Xu Chu
cs.CL · cs.LG
Large Language Models (LLMs) can exhibit diverse personas, and activating expert personas has been shown to improve domain expertise and task accuracy. However, existing persona control methods often suffer from cross-domain coupling, which may lead to overly aggressive behavior in high-caution domains such as healthcare, or excessive conservatism in risk-sensitive domains such as financial trading. To address this issue, we propose FOCUS...
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15
SkillZip: Contract-Preserving Graph Compression for Scalable Agent Skill Libraries
Xingyu Tan, Xiaoyang Wang, Qing Liu, Xiwei Xu, Xin Yuan, Liming Zhu, Wenjie Zhang
cs.CL · cs.AI
Large Language Models (LLMs) increasingly act as agents whose procedural knowledge is stored in reusable skill packages and loaded at inference time. As skill libraries grow, a central challenge is to expose the smallest sufficient executable context under a limited context budget. Existing systems struggle to reuse routines below the whole-skill level, preserve procedural contracts during compression, keep compressed routines executable and...
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