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MT-OSC: Path for LLMs that Get Lost in Multi-Turn Conversation
arXiv:2604.08782v3 Announce Type: replace Abstract: Large language models (LLMs) suffer significant performance degradation when user instructions and context are distributed over multiple conversational turns, yet multi-turn (MT) interactions dominate chat interfaces. The routine approach of appending full chat history to prompts rapidly exhausts context windows, leading to increased latency, higher computational costs, and diminishing returns as conversations extend. We introduce MT-OSC, a...
Park ranger’s death being investigated after falling into crevasse on Alaska’s Mt. McKinley
Park ranger’s death being investigated after falling into crevasse on Alaska’s Mt. McKinley Denali, federally designated as Mount McKinley, is the highest mountain peak in North America - Bookmark A seasonal park ranger has died after falling into a crevasse on Mount McKinley, North America's tallest mountain, the National Park Service announced. Robin Pendery, from Enumclaw, Washington, was a mountaineering ranger assigned to Denali National Park and Preserve. She fell on Thursday while on...
MT-EditFlow: Reinforcement Learning for Multi-Turn Image Editing with Flow Matching
Announce Type: new Abstract: Recent breakthroughs in instruction-based image editing have captured significant attention, as models are now capable of handling real-world editing demands with the practicality required by everyday users. However, editing models trained primarily for single-turn edits often break down in multi-turn editing--the natural interactive setting where a user iteratively refines an image based on the model's own previous outputs. This failure stems from the...
TimeSage-MT: A Multi-Turn Benchmark for Evaluating Agentic Time Series Reasoning
Announce Type: new Abstract: Time series data inform critical decisions across many real-world domains. While large language model (LLM) agents can analyze data through natural language and tools, it remains unclear whether they can conduct reliable time series analysis across multi-turn conversations. Existing benchmarks focus on single-step tasks such as forecasting and anomaly detection, overlooking practical workflows where user goals evolve, agents must build on prior analyses, and...
G^2C-MT: Graph-Guided Context Selection for Document-Level Machine Translation
arXiv:2606.03078v1 Announce Type: new Abstract: Effective document-level machine translation (DocMT) requires capturing long-range discourse dependencies. Recent work has explored retrieval-based and discourse-aware context selection. However, these approaches often lack an explicit mechanism for modeling structured discourse dependencies between distant paragraphs in a document.
Socialite faces court over alleged drink spiking at Mt Buller
Socialite Amy Tossoun faces court over alleged Mount Buller drink spiking Wed 10 Jun 2026 at 6:07pm In short: Amy Tossoun appeared in Mansfield Magistrates court today over charges relating to an alleged drink spiking incident at Mount Buller. Both the prosecutors and defence lawyers called for a diversion plan to be put in place, however Magistrate Amina Bhai said the offences were too serious for a diversion. The case will return to court in October.
Dynamic Meta-Metrics: Source-Sentence Conditioned Weighting for MT Evaluation
arXiv:2605.09098v2 Announce Type: replace Abstract: We propose Dynamic Meta-Metrics (DMM), a framework for machine translation evaluation that learns source-sentence conditioned combinations of existing metrics. Rather than relying on a single static ensemble or language-specific weighting, DMM adapts the metric combination based on properties of the source segment. We study hard conditioning, which fits an interpretable combiner per cluster, and an exploratory soft-conditioned extension...
Rewrite to Translate, Translate to Reward: Reinforcement Learning for Source Rewriting in Machine Translation
arXiv:2606.08011v1 Announce Type: new Abstract: Although directly prompting off-the-shelf Large Language Models (LLMs) to generate meaning-preserving source rewrites can effectively enhance Machine Translation (MT) quality, doing so requires manually tuning prompts for different MT models. In this work, we propose RLSR (Reinforcement Learning for Source Rewriting), a novel RL-based framework for training a source rewriting model without tuning prompts for each MT model.
Translation Heads: Disentangling meaning from language in LLM-based machine translation
Announce Type: replace Abstract: Mechanistic Interpretability (MI) seeks to explain how neural networks implement their capabilities, but the scale of Large Language Models (LLMs) has limited prior MI work in Machine Translation (MT) to word-level analyses. We study sentence-level MT from a mechanistic perspective by analyzing attention heads to understand how LLMs internally encode and distribute translation functions. We decompose MT into two subtasks: producing text in the target language...
α-Synuclein and γ-Tubulin Cooperatively Regulate Activity-Evoked Presynaptic Microtubule Nucleation to Gate Dopamine Release
alpha-Synuclein has long been implicated in the regulation of synaptic activity, but the molecular basis that underlies this function has been elusive. Here, we identify a microtubule (MT)-dependent mechanism through which alpha-synuclein regulates synaptic dopamine release. Using live imaging of cultured dopaminergic neurons, we visualize dynamic MTs at individual presynaptic boutons and show that neuronal activity triggers local gamma-tubulin-dependent MT nucleation.