You're viewing the US edition You can switch to the UK or AU homepage at any time using this menu. US Army U-turn over futuristic drone and robot unit is slammed as'deeply dangerous' This is an epic humiliation for Harry and Meghan. But these two are dangerous... we should fear the twisted real strategy behind their UK return: MAUREEN CALLAHAN America's pancreatic cancer explosion: Science breakthrough leaves experts fearing surprisingly common lifestyle habit is behind surge in young patients... as new early warning sign is revealed Trump's under-siege aide Natalie Harp caught in foul-mouthed explosion as insiders reveal White House whispers about her close access to president Sussexes'already have a house lined up to live in... and it's not a Royal residence' A close friend of Trump has told me the truth about his young aide Natalie. There's a reason the whispers are getting louder: KENNEDY Hayden Panettiere's LA neighbors were warned to call police if they saw her ex Brian Hickerson in the past... as DEA reportedly joins investigation into star's death I was mocked a year ago when I warned there was an Establishment plot to bring Harry and Meghan back to Britain. Now I've been proven right... and my sources have made it clear this is only the beginning: RICHARD EDEN Surrogate who refused to abort baby in fresh battle with biological parents over child's NAME Why loyal Trump aide Natalie Harp has only one pair of shoes - and she can even drive a golf cart in them! The'affair mode' phone settings that all cheaters use: I knew my partner was up to something... here's how I cracked his secret code and uncovered all his dirty antics Read Brian Hickerson's sinister texts to girlfriend Hayden Panettiere as he breaks cover after star's death: 'Time to go to war' Shocking moment 16-year-old girl storms field and tackles middle school football player... before being hit with assault charges and lifetime ban Virginia mother stabbed to death on jogging trail'by teenage migrant coworker' who was caught entering US illegally two years ago... then released Brooklyn Beckham's pizza-making post is flooded with pleas from fans as he shares cooking video hours after brother Romeo attended Italian culinary class Common brain disorder suffered by millions is often dismissed as being'all in your head' READ MORE: Life skill that reduces chances of dying from Alzheimer's disease By the time many people with functional neurological disorder - a condition in which there is a problem with how the brain sends and receives signals - arrive at a doctor's office, they have heard these five words more times than they can count.

When a graduate found a baby Florida scrub millipede, she put it in a kiddie pool. Then it got busy reproducing. Breakthroughs, discoveries, and DIY tips sent six days a week. While Florida is perhaps best known for its beaches and wetlands, its landscape hosts other notable features: ridges . Millions of years ago, sea levels were higher than they are today, and these elevated areas of land became like islands.

This paper addresses a motion planning problem to achieve spatio-temporal-logical tasks, expressed by syntactically co-safe linear temporal logic specifications (scLTL\next), in uncertain environments. Here, the uncertainty is modeled as some probabilistic knowledge on the semantic labels of the environment. For example, the task is "first go to region 1, then go to region 2"; however, the exact locations of regions 1 and 2 are not known a priori, instead a probabilistic belief is available. We propose a novel automata-theoretic approach, where a special product automaton is constructed to capture the uncertainty related to semantic labels, and a reward function is designed for each edge of this product automaton. The proposed algorithm utilizes value iteration for online replanning. We show some theoretical results and present some simulations/experiments to demonstrate the efficacy of the proposed approach.

Large language models are often described as capable of reflective reasoning, yet recursive self-evaluation without external feedback frequently yields reformulation rather than progress. We test this prediction in a cross-provider study of 144 reasoning sequences across three models (OpenAI GPT-4o-mini, Anthropic Claude 3 Haiku, and Google Gemini 2.0 Flash) and four task families (arithmetic, code, explanation, reflection), each iterated ten times under two conditions: ungrounded self-critique and a minimal grounding intervention (a single verification step at iteration three). Mean informational change (delta I, measured via normalized edit distance) declined by 55% from early (0.193) to late (0.087) iterations in ungrounded runs, with consistent patterns across all three providers. Grounded runs showed a +28% rebound in informational change immediately after the intervention and sustained non-zero variance thereafter. Complementary measures-n-gram novelty, embedding drift, and character-level entropy-converged on the same pattern: reflection without contact tends toward informational closure. We interpret this as evidence for a structural limit on self-correction in generative reasoning: without an exchange of information with an independent verifier or environment, recursive inference approaches an attractor state of epistemic stasis. Minimal grounding functions as dissipative coupling, reintroducing informational flux. The cross-architecture consistency suggests the mirror loop arises from shared autoregressive training objectives rather than provider-specific alignment schemes. The results delineate when reflection is performative rather than epistemic and motivate design principles for grounded, cooperative reasoning. Materials and code are publicly available.

Large Reasoning Models (LRMs) excel at complex reasoning but are traditionally evaluated in static, "frozen world" settings: model responses are assumed to be instantaneous, and the context of a request is presumed to be immutable over the duration of the response. While generally true for short-term tasks, the "frozen world" assumption breaks down in modern reasoning tasks such as assistive programming, where models may take hours to think through problems and code may change dramatically from the time the model starts thinking to the model's final output. In this work, we challenge the frozen world assumption and evaluate LRM robustness under two realistic dynamic scenarios: interruptions, which test the quality of the model's partial outputs on a limited budget, and dynamic context, which tests model adaptation to in-flight changes. Across mathematics and programming benchmarks that require long-form reasoning, static evaluations consistently overestimate robustness: even state-of-the-art LRMs, which achieve high accuracy in static settings, can fail unpredictably when interrupted or exposed to changing context, with performance dropping by up to 60% when updates are introduced late in the reasoning process. Our analysis further reveals several novel failure modes, including reasoning leakage, where models fold the reasoning into their final answer when interrupted; panic, where under time pressure models abandon reasoning entirely and return incorrect answers; and self-doubt, where performance degrades while incorporating updated information. Project Page: http://dynamic-lm.github.io/

We thank reviewers for the relevant comments. We first address general questions and then give brief individual answers. Those projected distributions vary smoothly as they are driven by an ODE. Continuous-time Bayesian networks (Nodelman et al., UAI 2002) address a This joint modeling of continuous measurements and events was left for future work. Some assumptions have to be made about the conditional distribution of the observations.

Prior work has largely treated future event prediction as a static task, failing to consider how forecasts and the confidence in them should evolve as new evidence emerges. To address this gap, we introduce EVOLVECAST, a framework for evaluating whether large language models appropriately revise their predictions in response to new information. In particular, EVOLVECAST assesses whether LLMs adjust their forecasts when presented with information released after their training cutoff. We use human forecasters as a comparative reference to analyze prediction shifts and confidence calibration under updated contexts. While LLMs demonstrate some responsiveness to new information, their updates are often inconsistent or overly conservative. We further find that neither verbalized nor logits-based confidence estimates consistently outperform the other, and both remain far from the human reference standard. Across settings, models tend to express conservative bias, underscoring the need for more robust approaches to belief updating.

Deliberation plays a crucial role in shaping outcomes by weighing diverse perspectives before reaching decisions. With recent advancements in Natural Language Processing, it has become possible to computationally model deliberation by analyzing opinion shifts and predicting potential outcomes under varying scenarios. In this study, we present a comparative analysis of multiple NLP techniques to evaluate how effectively models interpret deliberative discourse and produce meaningful insights. Opinions from individuals of varied backgrounds were collected to construct a self-sourced dataset that reflects diverse viewpoints. Deliberation was simulated using product presentations enriched with striking facts, which often prompted measurable shifts in audience opinions. We have given comparative analysis between two models namely Frequency-Based Discourse Modulation and Quantum-Deliberation Framework which outperform the existing state of art models. Deliberation is the structured process of reasoning, dialogue, and weighing evidence before decisions are made. Unlike ordinary conversation, it emphasizes logical argumentation, inclusivity, and critical reflection.

Effective long-term memory in conversational AI requires synthesizing information across multiple sessions. However, current systems place excessive reasoning burden on response generation, making performance significantly dependent on model sizes. We introduce PREMem (Pre-storage Reasoning for Episodic Memory), a novel approach that shifts complex reasoning processes from inference to memory construction. PREMem extracts fine-grained memory fragments categorized into factual, experiential, and subjective information; it then establishes explicit relationships between memory items across sessions, capturing evolution patterns like extensions, transformations, and implications. By performing this reasoning during pre-storage rather than when generating a response, PREMem creates enriched representations while reducing computational demands during interactions. Experiments show significant performance improvements across all model sizes, with smaller models achieving results comparable to much larger baselines while maintaining effectiveness even with constrained token budgets. Code and dataset are available at https://github.com/sangyeop-kim/PREMem.