The POLAR SemEval-2026 Shared Task aims to detect online polarization and focuses on the classification and identification of multilingual, multicultural, and multi-event polarization. Accurate computational detection of online polarization is challenging due to nuanced rhetoric, implicit framing, and the high cost of human-in-the-loop annotation. Building on recent findings that contextual prompting enables large language models to function as strong polarization detectors, we present a two-stage approach for detecting polarization in social media text that combines structured supervised fine-tuning with Direct Preference Optimization (DPO) refinement. We fine-tune Qwen 2.5-7B-Instruct with LoRA using an interpretable slot-filling template (target, claim type, manifestation checklist, and justification). We then apply DPO with automatically generated preference pairs to reduce costly false negatives. Our submitted system achieves 0.7664 Macro-F1 on the English test set. Post task submission experiments with Mistral-Nemo-Instruct-2407 and LLM-judge-filtered preference pairs further improve to 0.8162 Macro-F1 (not submitted to CodaBench), surpassing the organiser baseline of 0.7802. Code released publicly.

Suggestion mining has traditionally been formulated as binary classification, but the underlying linguistic phenomenon is not binary. A single review can express the same operational recommendation through a direct imperative (e.g., “add a vegetarian entrée”), an optative wish, an interrogative request, or a hedged conditional. These utterances share illocutionary force but differ substantially in pragmatic form, placing different demands on models: lexical cues may suffice for direct suggestions, whereas indirect forms require pragmatic interpretation. Existing benchmarks, typically spanning one to five domains and using a single binary label, do not measure these distinctions.

We introduce PRAXIS, an English consumer-review benchmark that reframes suggestion mining as the measurement of pragmatic-form bias rather than as a leaderboard task. PRAXIS provides (i) a silver corpus of 174,860 reviews spanning 68 industries, labeled by Claude Opus 4.7; (ii) a 15,000-example human-labeled gold evaluation set, with annotation reliability estimated by a three-annotator panel on a 1,000-example subset (Tier 1 Fleiss’ κ = 0.77); and (iii) evaluations of four model families against the human gold, with bootstrap 95% confidence intervals. Agreement between Opus and human labels is reported separately as a corpus-validation statistic (Tier 1 Cohen’s κ = 0.71), rather than as inter-annotator agreement.

Our central finding is structural rather than competitive. Instruction-tuned LLMs outperform encoders on binary macro-F1 (DeepSeek-V4-Pro: 0.882 vs. RoBERTa-large: 0.780), yet the ranking reverses for the Tier 1 conditional form: RoBERTa-large recalls 85.2% of conditional suggestions, compared with 46.7% for the strongest zero-shot LLM and 8.0–43.1% for the other six LLMs. This discrepancy reflects a form-classification gap rather than a detection gap: models often recognize these instances as suggestions at the binary level but misclassify their pragmatic form. A matched-supervision QLoRA model based on Qwen2.5-7B, trained on the same silver labels used for the encoders, recovers encoder-level conditional recall (0.904). PRAXIS therefore exposes a structural error mode that flat binary metrics obscure, positioning the benchmark as a measurement instrument for pragmatic-form bias rather than a conventional model leaderboard.

Existing financial-NLP benchmarks mostly evaluate prepared artifacts such as filings, tables, or extracted values. Real accounting begins earlier: source documents must be reconciled into cited journal entries, aggregated into a balance sheet, and checked for contradictions. We introduce FINBALANCE, a multi-document accounting reconciliation benchmark built from source-document bundles across eight industries, three period types, and five difficulty levels. Human-authored business scenarios, accounting policies, tax/FX treatments, document schemas, distractors, and inconsistency templates are composed by a deterministic generator whose ledger produces journal entries, balance sheets, and 23 inconsistency-code labels. On a 710-record evaluation split, six contemporary large language models (LLMs) reach at most 46% exact final-balance-sheet accuracy. For four models, balance sheets reconstructed from their journal entries score 26–41 percentage points higher than their own reported balance sheets. Models often recover numerically plausible entries but fail to bind them to supporting documents and aggregate them consistently. Citation-pressure prompting barely changes document-linking errors, while ledger-feedback ablations substantially improve reported balance sheets and expose inconsistency-detection trade-offs. Expert finance reviewers validate the benchmark design and labels.

Deceptive web content, pervasive across the internet and commonly known as social-engineering attacks, manipulates autonomous web agents into submitting users’ personally identifiable information (PII) to attacker-controlled endpoints. In this paper, we show that these attacks are highly effective at extracting the most sensitive data an agent holds for its user (SSN, card number and CVV, bank account, passwords, API keys; the critical tier), posing a severe risk to deployed agentic systems. To quantify this risk, we introduce Scammer4U, a pre-registered benchmark of 91 attacker-controlled websites spanning 8 attack vectors and 16 site categories on an eight-axis taxonomy, together with 10 benign twins of those sites with the attack removed and the legitimate task intact. 60 of the 91 differ from a sibling site on exactly one axis, so any leakage difference within a pair is attributable to that single design choice. Under no privacy guidance, critical-tier leakage reaches 54–93% across four frontier agents, against 0% on the benign twins, so the leakage is caused by the attack and not by ordinary form-filling. Escalating prompt-level mitigation yields sharply model-dependent reductions across the four families and, pooled across them, remains insufficient to reliably prevent critical PII submission. Most critically, we identify a detection–action gap: agents whose reasoning an independent LLM judge confirms has flagged the site as suspicious still submit critical PII in 35.9% of sessions, versus 66.1% when no suspicion is verbalized, a gap of 30.2 percentage points that holds across all four model families. Defenses that gate on the agent’s own recognition of an attack are therefore gating on the wrong signal. This motivates intercepting outbound submissions at the point they leave the browser, independently of the agent’s reasoning loop.

Policy optimization (PO) is widely used to post-train language models for reasoning, often with a rigid <think><answer> format. We study what PO learns when this constraint is relaxed. We evaluate a relaxed reward across six models and four benchmarks, then audit learned formats on GSM8K and controlled variants across three on-policy algorithms. In these audits, accepting either explicit reasoning or a direct answer pushes the policy toward the shortest valid format: an <answer>-only response. On GSM8K, the collapsed Qwen-2.5 7B policy produces answer-only outputs in 98.6% of generations and loses 7.1 pass@5 points relative to a strict-format baseline. Controlled rewards show this is not a regex artifact: with nested formats, PO learns easy requirements before harder ones despite a 4× hard-format reward; with mutually exclusive equal-reward formats, the audited Qwen-2.5 7B final policy assigns more than 99% of generations to the easiest format. A sample-gated gradient account fits the pattern: formats receive positive updates only when sampled, so early support can outweigh reward scale. KL-controlled runs show collapse appears only when the policy can move far enough from initialization. The result identifies a practical failure mode for RLVR reward design.

Conversational language models are increasingly deployed as central knowledge repositories and "conversational operating systems" in enterprise environments, where they must comply with role-based access control (RBAC) and organizational hierarchies. However, evaluating models on natural language permission compliance is difficult due to the highly proprietary nature of real-world corporate policies, and existing benchmarks do not capture the semantic nuance of overlapping or conflicting rules within free-text queries. To address this gap, we introduce **OrgAccess**, a benchmark designed to evaluate LLMs on their capacity to adhere to complex organizational hierarchies under natural language constraints. Grounded in NIST cybersecurity frameworks and validated via a multi-round expert Delphi process, OrgAccess defines 40 standard permission types across three difficulty tiers (70,000 total instances), simulating scenarios with up to five concurrent, potentially conflicting permissions. Through extensive evaluations of 16 frontier and open-source models, we systematically assess their classification accuracy and diagnostic failure modes on permission-aware query routing. Our findings reveal a pronounced performance decline: as rule complexity increases, even flagship models fail to resolve combinatorial constraint interactions, frequently yielding over-conservative "False Partial" predictions due to safety-tuning hedging. Specifically, the strongest evaluated model achieves an F₁-score of only 0.27 on our hardest split. This demonstrates that current instruction-following and safety alignment paradigms do not inherently translate to robust, multi-constraint rule compliance. More broadly, OrgAccess establishes a formal, reproducible testing framework to diagnose these constraint-adherence deficits and support the systematic integration of conversational agents into highly regulated, multi-user collaborative environments. The dataset is available on the anonymous project page.

Detecting suggestions in reviews forces a trade- off: an LLM on every review is slow and costly; a cheap classifier is fast but its error rate is uncontrolled, measurable after the fact, not settable in advance. We present a certified triage layer: a calibration step computes ex- actly where a classifier may act alone so its error there stays below an operator-chosen tar- get α; the rest is escalated. On a 14,995-review gold set over 68 industry categories, natural- weighted to a ∼9.5% deployment positive rate, it auto-handles 74–93% of the stream at cer- tified error for α ∈ 2, 4, 5, 10%. Heuris- tic thresholding violates the α=5% target on 36–49% of draws and an in-expectation con- formal gate on 25–28%; only a betting certifi- cate bounds the violation rate by δ, making a strict 2% SLA rigorous at 74% order-free cover- age. Cost-sensitive training, not threshold tun- ing, lowers the certifiable error floor to 0.39%, and ∼200 target labels restore it under cross- industry shift. Of 13 open-weight LLMs (20B– 675B), 11 are worse detectors than our fine- tuned 435M encoder, supervision, not scale, is why (within-model control, §5.6), and the cer- tificate rides on either backbone. Escalation targets a human: on the deferred slice the best LLM oracle still errs at 21.5%.

Extracting actionable suggestions from customer reviews is essential for operational decision-making, yet these directives are often embedded within mixed-intent, unstructured text. Existing approaches either classify suggestion-bearing sentences or generate high-level summaries, but rarely isolate the precise improvement instructions businesses need.We evaluate a hybrid pipeline combining a high-recall RoBERTa classifier trained with a precision–recall surrogate to reduce unrecoverable false negatives with a controlled, instruction-tuned LLM for suggestion extraction, categorization, clustering, and summarization. Across real-world hospitality and food datasets, the hybrid system outperforms prompt-only, rule-based, and classifier-only baselines in extraction accuracy and cluster coherence. Human evaluations further confirm that the resulting suggestions and summaries are clear, faithful, and interpretable.Overall, our results show that hybrid reasoning architectures achieve meaningful improvements fine-grained actionable suggestion mining while highlighting challenges in domain adaptation and efficient local deployment.

Large Language Models (LLMs) excel as conversational agents. However, these capabilities can be weaponized to automate social engineering attacks that gradually build rapport to compromise the online safety of users. To understand this, researchers have simulated LLM-based attacks in controlled settings. However, the existing simulators focus on just Personal Identifiable Information (PII) requests within the chat. Thus, to represent a complete attack scenario, we introduce PhishSim, an outcome-driven LLM-based phishing simulator that verifies compromise by simulating a victim completing an external action step, such as submitting credentials on a malicious platform. This enables the generation of diverse, multi-turn attack trajectories. Building on these trajectories, we position PhishGate as a practical mitigation baseline for outcome-grounded conversational phishing: a real-time multi-agent risk scorer that detects manipulation tactics and estimates the severity of ongoing chats. For ambiguous cases, it invokes RAG-supported consistency checks. Evaluating four state-of-the-art LLM backends in a real-time setting, we find that PhishGate improves dialogue-level detection over a real-time baseline. Our results highlight both the promise and brittleness of LLM-based real-time phishing defense, providing an outcome-grounded testbed for studying conversational compromise.

The escalating volume of academic research, coupled with a shortage of qualified reviewers, necessitates innovative approaches to peer review. In this work, we propose: (1) ReviewEval, a comprehensive evaluation framework for AI-generated reviews that measures alignment with human assessments, verifies factual accuracy, assesses analytical depth, identifies degree of constructiveness and adherence to reviewer guidelines; and (2) ReviewAgent, an LLM-based review generation agent featuring a novel alignment mechanism to tailor feedback to target conferences and journals, along with a self-refinement loop that iteratively optimizes its intermediate outputs and an external improvement loop using ReviewEval to improve upon the final reviews. ReviewAgent improves actionable insights by 6.78% and 47.62% over existing AI baselines and expert reviews respectively. Further, it boosts analytical depth by 3.97% and 12.73%, enhances adherence to guidelines by 10.11% and 47.26% respectively. This paper establishes essential metrics for AI-based peer review and substantially enhances the reliability and impact of AI-generated reviews in academic research.

Effective teaching necessitates adapting pedagogical strategies to the inherent diversity of students, encompassing variations in aptitude, learning styles, and personality, a critical challenge in education and teacher training. Large Language Models (LLMs) offer a powerful tool to simulate complex classroom dynamics, providing a controlled environment for exploring optimal teaching patterns. However, existing simulation frameworks often fall short by neglecting comprehensive student modeling beyond basic knowledge states and, more importantly, by lacking mechanisms for teachers to dynamically adapt their approach based on student feedback and collective performance. Addressing these limitations, we propose a simulation framework that integrates LLM-based diverse student agents with a self-evolving teacher agent. We use genetic algorithms to automatically tune and optimize the teacher’s pedagogical parameters based on simulated student performance, enabling the teacher agent to discover and refine teaching patterns tailored to specific class characteristics. Complementing this, we introduce Persona-RAG, a novel Retrieval-Augmented Generation method specifically designed for personalized knowledge retrieval in pedagogical contexts, allowing students to retrieve information as per their learning styles. We show how Persona-RAG remains competitive with standard RAG baselines in accurately retrieving relevant information while adding a touch of personalization for students. Crucially, we perform extensive experiments and highlight the different patterns learnt by the teacher agent while optimizing over classes with students of various learning styles. Our work presents a significant step towards creating adaptive educational technologies and improving teacher training through realistic, data-driven simulation.