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Predicting earnings surprises through the analysis of earnings conference call transcripts has attracted increasing attention from the financial research community. Conference calls serve as critical communication channels between company executives, analysts, and shareholders, offering valuable forward-looking information. However, these transcripts present significant analytical challenges, typically containing over 5,000 words with substantial redundancy and industry-specific terminology that creates obstacles for language models. In this work, we propose the Sparse Autoencoder for Financial Representation Enhancement (SAE-FiRE) framework to address these limitations by extracting key information while eliminating redundancy. SAE-FiRE employs Sparse Autoencoders (SAEs) to efficiently identify patterns and filter out noises, and focusing specifically on capturing nuanced financial signals that have predictive power for earnings surprises. Experimental results indicate that the proposed method can significantly outperform comparing baselines.

ACL 2026

SAE-FiRE: Enhancing Earnings Surprise Predictions Through Sparse Autoencoder Feature Selection

EMNLP 2021 is planned to be a hybrid event in Punta Cana, Dominican Republic, with both on-site and fully virtual participation possible. The experience for on-site participants would closely approximate a normal pre-COVID *ACL conference, with 5-6 thematically organized parallel sessions and live Q/A and interactive discussion immediately after the talks. Presentations by virtual participants will be equitably interleaved with those of on-site participants, projected on the auditorium screens as if on-site, and also followed immediately by live Q/A and interactive discussion at a time during reasonable waking hours for the virtual presenter. For all participants, on-site and virtual, who are unable to attend a session due to either time-zone issues or because they are participating in another session live, talk recordings and slides will be available online at a minimum after the live presentation (and in many cases before as well), and questions may be submitted in advance on session-specific discussion boards and answered live in session with the usual visual aids if desired.

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EMNLP 2021

EMNLP 2021 is planned to be a hybrid event in Punta Cana, Dominican Republic, with both on-site and fully virtual participation possible.

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It is my great pleasure and honor to welcome you to the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026), held in San Diego, California, USA, July 2–7, 2026. ACL 2026 continues our fields tradition of excellence in scholarship, innovation, and inclusivity, and I am deeply grateful to the many volunteers who have worked tirelessly to bring this event to life.

It is exciting to see the many developments in the field reflected at our conference, such as the advancement of large language models (born out of our community) into many application directions. This is not without challenges, partly due to unsolved problems, and partly due to misuse of the technology. All this is reflected well at ACL 2026. The conference also reflects that the field is not only about advancing technology, but also the science of fundamentally understanding language and how it is use by many different speaker communities. <br>
[Full Message from General Chair Philipp Koehn](https://drive.google.com/file/d/1QiJFOmMjvDgsDo3i1XzlQnImxe3RU_Cy/view?usp=sharing) <br><br><br>
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The 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026) will take place in San Diego, California, from July 2 - 7, 2026.

Reasoning language models are controlled through explicit modes such as Think and No-think, yet we find that these behaviors are largely governed by a few token-level triggers rather than high-level instructions. Through attention analysis and controlled prompting experiments, we show that a leading ``Okay'' token induces reasoning behavior, while the newline pattern following `</think>` suppresses it. Based on this observation, we propose Mid-Think, a simple training-free prompting format that combines these triggers to achieve intermediate-budget reasoning, consistently outperforming fixed-token and prompt-based baselines in terms of the accuracy--length trade-off. Furthermore, applying Mid-Think to RL training after SFT reduces training time by approximately 15% while improving final performance of Qwen3-8B on AIME from 69.8% to 72.4% and on GPQA from 58.5% to 61.1%, demonstrating its effectiveness for both inference-time control and RL-based reasoning training.

 Mid-Think: Training-Free Intermediate-Budget Reasoning via Token-Level Triggers

State-of-the-art code generation frameworks rely on mental simulation, where LLMs internally trace execution to verify correctness. We expose a fundamental limitation: the Mental-Reality Gap---where models hallucinate execution traces and confidently validate buggy code. This gap manifests along two orthogonal dimensions: the Specification Gap (overlooking edge cases during planning) and the Verification Gap (hallucinating correct behavior for flawed code). We propose SolidCoder with a simple principle: don't imagine---execute. The S.O.L.I.D. architecture addresses both dimensions by forcing edge-case awareness before algorithm design and replacing imagined traces with sandboxed execution using property-based oracles. With GPT-4o, SolidCoder achieves state-of-the-art pass@1 performance: 95.7\% on HumanEval (+0.6\%p), 77.0\% on CodeContests (+4.3\%p), and 26.7\% on APPS (+3.4\%p). Ablation reveals that edge-case awareness provides the largest individual gain, while execution grounding catches categorically different errors that specification improvements cannot address. These gains generalize to RL post-trained models, validating that bridging both gap dimensions is essential for robust code synthesis. We release our code and framework to facilitate future research.

SolidCoder: Bridging the Mental-Reality Gap in LLM Code Generation through Concrete Execution

Over the years, scalar MT metrics have advanced rapidly on benchmarks. Yet they remain black boxes, offering little insight into their decisions and sometimes degrading under out-of-distribution inputs. We introduce Remedy-R, a reasoning-driven generative MT metric trained with reinforcement learning from pairwise translation preferences, without requiring error-span annotations or distillation from closed LLMs. Unlike scalar MT metrics that only outputs translation quality scores, Remedy-R produces step-by-step analyses of accuracy, fluency, and completeness, enabling more interpretable assessments. With only 60K pairwise training samples across two language pairs, Remedy-R remains competitive with top scalar metrics and GPT-4-based judges on WMT22–24 metric benchmarks, generalizes to other languages, and shows strong robustness on OOD stress tests. Moreover, Remedy-R generates self-reflective feedback that can be reused for translation refinement. We validate the faithfulness of such feedback with GPT-4 and show that a simple evaluate–revise pipeline leveraging Remedy-R’s analyses consistently improves translation quality across diverse models without any task-specific tuning.

Remedy-R: Generative Reasoning for Machine Translation Evaluation without Error Annotations

LLM agents are increasingly used for social simulation, yet emotion is often treated as a transient cue, causing emotional amnesia and weak long-horizon continuity. We present Sentipolis, a framework for emotionally stateful agents that integrates continuous Pleasure-Arousal-Dominance (PAD) representation, dual-speed emotion dynamics, and emotion--memory coupling. Across thousands of interactions over multiple base models and evaluators, Sentipolis improves emotionally grounded behavior, boosting communication, and emotional continuity. Gains are model-dependent: believability increases for higher-capacity models but can drop for smaller ones, and emotion-awareness can mildly reduce adherence to social norms, reflecting a human-like tension between emotion-driven behavior and rule compliance in social simulation. Network-level diagnostics show reciprocal, moderately clustered, and temporally stable relationship structures, supporting the study of cumulative social dynamics such as alliance formation and gradual relationship change.

Sentipolis: Emotion-Aware Agents for Social Simulations

Large Language Models excel at role-playing but struggle to maintain consistent personalities across extended multi-turn interactions. We propose PersonaForge, combining (1) a three-layer personality architecture grounded in psychological theory and (2) a dual-process generation mechanism inspired by cognitive science. We test two falsifiable claims: Claim 1 (Orthogonality): Psychology-grounded dimensions (Big Five + Defense Mechanisms) provide more orthogonal constraints than natural language descriptions, reducing long-dialogue drift. Claim 2 (Integration Necessity): High-dimensional personality constraints create "production interference" requiring a cognitive workspace (Inner Monologue) to resolve—removing it degrades performance below simpler baselines. Experiments on 88 characters demonstrate: (1) +19.4% personality consistency (PC) with human correlation r=0.82, (2) reduced drift over 50-turn conversations (6.3% vs. 24.8% baseline), and (3) +64.7% defense mechanism manifestation. External validation on RoleBench confirms generalization (73.2% win-rate, drift 8.4% vs. 20.4%). Selective dual-process activation achieves 96% of full-system performance with only 13.4% token overhead. Human evaluation confirms more authentic and psychologically coherent character behaviors. Code and data: https://github.com/fQwQf/PersonaForge.

PersonaForge: Psychology-Grounded Dual-Process Architecture for Personality-Consistent Role-Playing Agents

Long-horizon agents operate over extended sequences of reasoning and actions, but this inevitably accumulates context noise, resulting in excessive computational cost and information overload. Existing approaches commonly rely on fixed, rule-based summarization strategies (e.g., summarizing every few steps), which are inflexible, lack generalization, and often introduce irreversible information loss. We propose Self-Sum, a framework that empowers agents to autonomously decide when and what to summarize by modeling summarization as a first-class internal cognitive action, unified with external environmental actions within a multi-turn decision-making process. Specifically, we introduce a two-stage training recipe consisting of (i) a cold-start supervised fine-tuning stage that bootstraps summarization behavior, and (ii) a lightweight, summarization-aware reinforcement learning stage that refines summarization timing and content while discouraging unnecessary summaries. Experiments on multiple long-horizon benchmarks show that Self-Sum consistently outperforms no-summarization and rule-based baselines, with particularly strong gains in generalization. Analysis further reveals that Self-Sum learns to summarize sparsely at meaningful moments and preserves task-relevant information, highlighting the importance of jointly learning when and what to summarize for robust long-horizon agent behavior.

Self-Sum: Teaching an Agent to Decide Itself When and What to Summarize

Large Language Models (LLMs) are commonly trained on multilingual corpora that include Greek, yet reliable evaluation benchmarks for Greek—particularly those based on authentic, native-sourced content—remain limited. Existing datasets are often machine-translated from English, failing to capture Greek linguistic and cultural characteristics. We introduce GreekMMLU, a native-sourced benchmark for massive multitask language understanding in Greek, comprising 21,805 multiple-choice questions across 45 subject areas, organized under a newly defined subject taxonomy and annotated with educational difficulty levels spanning primary to professional examinations. All questions are sourced or authored in Greek from academic, professional, and governmental exams. We publicly release 16,857 samples and reserve 4,948 samples for a private leaderboard to enable robust and contamination-resistant evaluation. Evaluations of over 80 open- and closed-source LLMs reveal substantial performance gaps between frontier and open-weight models, as well as between Greek-adapted models and general multilingual ones. Finally, we provide a systematic analysis of factors influencing performance—including model scale, adaptation, and prompting—and derive insights for improving LLM capabilities in Greek.

 GreekMMLU: A Native-Sourced Multitask Benchmark for Evaluating Language Models in Greek

Both humans and large language models (LLMs) exhibit content effects: biases in which the plausibility of the semantic content of a reasoning problem influences judgments regarding its logical validity. While this phenomenon in humans is best explained by the dual-process theory of reasoning, the mechanisms behind content effects in LLMs remain unclear. In this work, we address this issue by investigating how LLMs encode the concepts of validity and plausibility within their internal representations. We show that both concepts are linearly represented and strongly aligned in representational geometry, leading models to conflate plausibility with validity. Using steering vectors, we demonstrate that plausibility vectors can causally bias validity judgements, and vice versa, and that the degree of alignment between these two concepts predicts the magnitude of behavioral content effects across models. Finally, we construct debiasing vectors that disentangle these concepts, reducing content effects and improving reasoning accuracy. Our findings advance understanding of how abstract logical concepts are represented in LLMs and highlight representational interventions as a path toward more logical systems.

 How Language Models Conflate Logical Validity with Plausibility: A Representational Analysis of Content Effects

While Large Language Model (LLM) agents show promise in automated trading, they still face critical limitations. Prominent multi-agent frameworks often suffer from inefficiency, produce inconsistent signals, and lack the end-to-end optimization required to learn a coherent strategy from market feedback. To address this, we introduce **AlphaQuanter**, a single-agent framework that uses reinforcement learning (RL) to learn a dynamic policy over a transparent, tool-augmented decision workflow, which empowers a single agent to *autonomously orchestrate tools* and *proactively acquire information* on demand, establishing a transparent reasoning process. Extensive experiments demonstrate that AlphaQuanter achieves state-of-the-art performance on key financial metrics. Besides, human evaluation shows the learned reasoning patterns reveal more faithful and coherent tool-usage behaviors, providing steps toward verifiable LLM-driven trading. Our code and data can be found at https://github.com/horizon-llm/AlphaQuanter.

AlphaQuanter: An End-to-End Tool-Augmented Agentic Reinforcement Learning Framework for Stock Trading

Group Relative Policy Optimization (GRPO) has become a standard approach for training mathematical reasoning models; however, GRPO training is computationally intensive and usually takes a long time, which consumes substantial computational resources and creates barriers for academic researchers and smaller organizations with limited GPU budgets. In this paper, we propose MMR-GRPO to accelerate GRPO training and reduce the overall training time required to reach peak performance, and the approach adopts Maximal Marginal Relevance
to reweigh rewards of multiple rollouts by balancing rollout quality with diversity to reduce rollout redundancy. The rationale is that redundant or similar completions, if repeatedly used to train a model, will create an “exploitation trap” and slow down model convergence in GRPO style reinforcement learning. Extensive evaluations across three model sizes (1.5B, 7B, 8B), three GRPO variants, and five mathematical reasoning benchmarks show that MMR-GRPO achieves comparable peak performance while requiring on average 47.9% fewer training steps and 70.2% less wall-clock time. These gains are consistent across models, methods, and benchmarks. Our code is released at: https://github.com/WeiKangda/MMR-GRPO.

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Mid-Think: Training-Free Intermediate-Budget Reasoning via Token-Level Triggers

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