Which AI stories has SIREN scored in this window? · What has SIREN scored under Capability? · What has SIREN scored as Papers? ·
Qwen-Audio-Jev: A Unified Interface for Audio Decisions
Large audio language models (LALMs) have made substantial progress in audio understanding. However, in high-throughput applications requiring frequent selection, judgment, and scoring, autoregressive…
Source: What has SIREN scored via arXiv cs.AI / cs.LG / cs.CL (newest)? · original · first seen by this index at
Names in this headline: Alibaba Qwen, What has SIREN scored about Jev?, What has SIREN scored about Qwen?. A name is a match against the published entity list, not a claim the subject wrote the story.
Publisher: What has SIREN scored from arxiv.org?. A publisher link means the original URL host cleared the outlet gate (≥5 scored stories in this window).
Topics: What has SIREN scored about Autoregressive models?, What has SIREN scored about AI throughput?, What has SIREN scored about AI latency?. A topic link means the title or summary matched a published needle with enough other stories to clear the tag gate.
What is the score on this story?
| Component | Points |
|---|---|
| source weight | 16.3 |
| recency | 14.7 |
| entities | 9.0 |
| corroboration | 0.0 |
| engagement | 0.0 |
| severity | 0.0 |
| coverage | 0.0 |
| Total | 40.0 |
Every term is published and every input is public, so this number can be recomputed. It ranks the item inside the 800-item window; it is not a judgement about importance in the world.
How many sources carried this story?Corroboration
Carried by one source. That is the normal case — most items are reported once — and it is stated rather than left blank so a single-source item cannot be mistaken for a corroborated one.
Which related stories are in this window?
- Intern Lumina U2: A Multi-Codebook Diffusion Large Language Model for Omni-Visual Understanding and Image Generation 47.5
- JevAgentHarness: Benchmarking, Deploying and Training Decision Models That Share Control with LLMs 46.2
- SWE-at-Scale: Scalable Issue Synthesis from Historical Bug Fixes 45.6
- PN-Mix: Pair-Norm Guided Mixed-Precision Key Cache Quantization for Efficient Long-Output LLM Inference 41.7
- Calibrated Typed Decisions for Entity, Relation, and Event Extraction 41.7
What questions do people actually ask?
What is the score on this story?
Scored 40.0 of 100. Every term is published and every input is public, so this number can be recomputed. It ranks the item inside the 800-item window — not a judgement about importance in the world and not an item doom score.
How many sources carried this story?
Carried by one source. That is the normal case — most items are reported once — and it is stated rather than left blank so a single-source item cannot be mistaken for a corroborated one.
Which related stories are in this window?
5 related stories in the same window, each with its own permanent scored page.
Does SIREN publish an item doom score?
No. Activity tempo, not probability of harm. A permanent story page carries a tempo score decomposition and corroboration set, not a probability of harm.