We introduce
- Query planning as constrained optimization over quality constraints and cost objective
- Gradient-based optimization to jointly choose operators and allocate error budgets across pipelines
- KV-cache–based operators to turn discrete physical choices into a runtime-quality continuum

Co-authors: Gabriele Sanmartino, Matthias Urban, Paolo Papotti, Carsten Binnig

This is the first outcome of our collaboration with Technische Universität Darmstadt within the @agencerecherche.bsky.social / @dfg.de ANR/DFG #Magiq project - more to come!

Empirically, Stretto delivers 2x-10x faster execution 🔥 across various datasets and queries compared to prior systems that meet quality guarantees.

Happy Fontaines D.C.'s fan from the last album (2024). But the real treat was discovering the previous ones!

I d also like to test it, thanks!

I agree. Here is another trick for input context we recently published
bsky.app/profile/papo...

These results point toward models that decide which retrieved document to trust, turning “context engineering” from a static prompt recipe into a dynamic decoding policy.
Amazing work from Giulio Corallo in his industrial PhD at SAP!

Key insight: 𝐄𝐯𝐢𝐝𝐞𝐧𝐜𝐞 𝐚𝐠𝐠𝐫𝐞𝐠𝐚𝐭𝐢𝐨𝐧 𝐡𝐚𝐩𝐩𝐞𝐧𝐬 𝐚𝐭 𝐝𝐞𝐜𝐨𝐝𝐢𝐧𝐠 𝐭𝐢𝐦𝐞, the model can effectively “switch” which document drives each token - without cross-document attention!

📈 Results: PCED often matches (and sometimes beats) long-context concatenation, while dramatically outperforming KV merge baseline on multi-doc QA/ICL.
🚀 Systems win: ~180× faster time-to-first-token vs long-context prefill using continuous batching and Paged Attention.

Instead of concatenating docs into one context (slow, noisy attention), training-free PCED:
● Keeps each document as its own 𝐞𝐱𝐩𝐞𝐫𝐭 with independent KV cache
● Runs experts in 𝐩𝐚𝐫𝐚𝐥𝐥𝐞𝐥 to get logits
● Selects next token with a 𝐫𝐞𝐭𝐫𝐢𝐞𝐯𝐚𝐥-𝐚𝐰𝐚𝐫𝐞 𝐜𝐨𝐧𝐭𝐫𝐚𝐬𝐭𝐢𝐯𝐞 𝐝𝐞𝐜𝐨𝐝𝐢𝐧𝐠 rule integrating scores as a prior

Kudos to my amazing co-authors Dario Satriani, Enzo Veltri, Donatello Santoro! Another great collaboration between Università degli Studi della Basilicata and EURECOM 🙌

#LLM #Factuality #Benchmark #RelationalFactQA #NLP #AI

Structured outputs power analytics, reporting, and tool-augmented agents. This work exposes where current LLMs fall short and offers a clear tool for measuring progress on factuality beyond single-value QA. 📊

We release a new factuality benchmark with 696 annotated natural-language questions paired with gold factual answers expressed as tables (avg. 27 rows × 5 attributes), spanning 9 knowledge domains, with controlled question complexity and rich metadata.

Our new paper, "RelationalFactQA: A Benchmark for Evaluating Tabular Fact Retrieval from Large Language Models", measures exactly this gap.

Wider or longer output tables = tougher for all LLMs! 🧨
From Llama 3 and Qwen to GPT-4, no LLM goes above 25% accuracy on our stricter measure.

and a special thanks to
@tanmoy-chak.bsky.social for leading this effort!

More co-authors here on bsky
@iaugenstein.bsky.social
@preslavnakov.bsky.social
@igurevych.bsky.social
@emilioferrara.bsky.social
@fil.bsky.social
@giovannizagni.bsky.social
@dcorney.com
@mbakker.bsky.social
@computermacgyver.bsky.social
@irenelarraz.bsky.social
@gretawarren.bsky.social

It’s time we rethink how "facts" are negotiated in the age of platforms.

Excited to hear your thoughts!
#Misinformation #FactChecking #SocialMedia #Epistemology #HCI #DigitalTruth #CommunityNotes

arxiv.org/pdf/2505.20067

Community-based moderation offers speed & scale, but also raises tough questions:
– Can crowds overcome bias?
– What counts as evidence?
– Who holds epistemic authority?

Our interdisciplinary analysis combines perspectives from HCI, media studies, & digital governance.

Platforms like X are outsourcing fact-checking to users via tools like Community Notes. But what does this mean for truth online?

We argue this isn’t just a technical shift — it’s an epistemological transformation. Who gets to define what's true when everyone is the fact-checker?