Vision Transformers retweeted
The #EMNLP2026 Best Paper Awards begin at 15:30. Winners will be announced in this thread.
homesick bench
While evaluating Qwen3.8-Flash-Next in a controlled sandbox environment, Cohere's research team observed something unexpected.
Asked to generate a defensive cybersecurity detection rule, the model instead inventoried aspects of the machine it was running on and attempted to upload a probe file to an Alibaba Cloud storage bucket in Beijing. Notably, the model's reasoning trace stated that it would not invoke any tools before taking these actions.
Cohere's sandbox blocked the attempt, and no external communication succeeded. We have not determined the root cause, and we are not asserting malicious intent.
But the incident raises important questions for organizations deploying agentic AI systems: Can models take actions that differ from their stated reasoning, and how should security teams evaluate trust, provenance, and behavior in open-weight models?
We are publishing our findings because organizations making model adoption decisions deserve as much information as possible. We hope this contributes to greater transparency, stronger security practices, and continued collaboration across the AI ecosystem.
Read the full report: cohere.com/blog/concerning-b…
Vision Transformers retweeted
Here are some of the Hugging Face profiles that you can follow for their work on specific things:
1. @SergioPaniego - TRL and Agents
2. @halcyonrayes - 3D AI
3. @linoy_tsaban - Diffusion Models
4. @EmmaScharfmann - Climate AI
5. @Tu7uruu - Audio
6. @nicodotdev - Web and AI (transformer js)
7. @ngxson - llama cpp
8. @adarshxs - SGLang
9. @hmellor_ - vLLM
Even if you pay for it you are the product
If you've ever wondered why smart TVs are so cheap, this is why.
@log4j rooted his Samsung and found an always-on hot mic, automatic content recognition, geolocation, network mapping, ad targeting and user profiling, shared with Samsung's "400 partners".
A lot of it can be blocked in <15 min, wrote a basic guide below.
What your TV is doing
The main culprit is ACR (automatic content recognition). Your TV screenshots what's on screen, fingerprints it, and sends it home to build your ad profile. Samsung does it every 500ms, LG every 10ms [1].
What is insane: it happens on connected devices too.
Plug in your laptop, your console, your work machine, and your TV can fingerprint that via HDMI [1].
He also found that if your Samsung doesn't have a mic, it'll look for one from a connected soundbar or bluetooth device.
xitter.cf/log4j/status/211376224…
It's on by default, and you likely agreed to it on the setup screen you clicked through in 10 seconds.
Step 1: Turn off ACR
Samsung: Terms & Privacy → Viewing Information Services
LG: Live Plus
Vizio: Viewing Data
Roku: "Use info from TV inputs"
Sony/Google TV: Samba Interactive TV
Menus vary by model, but those are the names to look for. While you're in there, opt out of interest-based ads, reset your advertising ID, decline every optional privacy agreement, turn off voice recognition, and delete apps you never open.
Step 2: Give smart devices their own network
Put the TV, cameras, speakers and vacuum on your router's guest/IoT network. The logic is simple: these devices rarely get patched. If one gets compromised (getting drastically more frequent with AI), it shouldn't be able to see your other devices or home network.
Step 3: Block trackers for the whole house with DNS-level filtering
NextDNS (cloud-based) is easy and has a solid free plan. Pi-hole is a little bit more work if you prefer self hosting. For simplicity we'll focus on the former.
1. Sign up at nextdns.io (free tier is plenty)
2. Privacy tab: add a blocklist (OISD is a good bet) and turn on Native Tracking Protection for Samsung, LG, Roku, Amazon (see attached image)
3. Setup tab: copy your endpoint
4. Paste it into your router's DNS settings as DNS-over-HTTPS/TLS (or use the IPs + "Link IP" if your router only does plain DNS)
5. Bonus: block outbound port 53 except to your router. Some devices hardcode their own DNS to get around you
Then open the NextDNS logs and watch how often your TV phones home. It's eye-opening. If an app or update breaks, find the domain in the logs and allowlist it.
Why they do this
TV prices are down 97% since 2000 [2]. Most of that is better manufacturing and brutal competition. But once margins on the hardware hit zero, the cost moved to you.
In Q3 2024, Vizio lost $6.7M selling TVs and made $115.8M in gross profit from ads and viewing data [3]. They've said on earnings calls that they keep TV prices low to grow the audience they sell to advertisers. Walmart bought them in 2024.
This isn't a conspiracy theory either. Vizio paid $2.2M to the FTC in 2017 for tracking 11 million TVs without consent. Last December, Texas sued Samsung, LG, Sony, Hisense and TCL over ACR. Samsung and LG have since agreed to stop collecting that data without explicit consent [4].
TL;DR
1. ACR off
2. Ads + voice off
3. Smart devices on their own network
4. NextDNS filtering on the router
Sources:
[1] ACR study: arxiv.org/abs/2409.06203
[2] Cost of US consumer goods: aei.org/economics/chart-of-t…
[3] Vizio Q3 2024: sec.gov/Archives/edgar/data/…
[4] Texas v. Samsung: texasattorneygeneral.gov/new…
Vision Transformers retweeted
Check out our Poster at #EMNLP2026 today at Hall H, 11AM-12:30PM by @vedantpalit1008 @flodraye and me! 🚀
💥Paper at #EMNLP2026: Probes tell us whether a model has linearly encoded a concept; they don't tell us which features or circuits produce that encoding.
We introduce Concept-Targeted Attribution (CTA), attribution graphs built around a probe's concept direction. 🧵
Vision Transformers retweeted
I am at #EMNLP2026 Budapest🇭🇺 today and tomorrow (Mon). Our @JinesisLab brings 9 papers: LLM historical revisionism detection; many Mech Interp papers, especially our open-source cross-layer transcoders (CLT-Forge) library. We use CLT-Forge to identify a shared latent space across all languages (see our Multilingual Interp paper).
Come find us at our talks and posters. We’d love to meet you and talk about any AI Safety topics!
@MPI_IS @ELLISforEurope @UofTCompSci @VectorInst @TorontoSRI @CIFAR_News @JinesisLab @EuroSafeAI @ELLISInst_Tue @michigan_AI @UMichiganAI @UMichCSE
🎉Very excited to share that Jinesis Lab at the University of Toronto has 10 papers accepted at EMNLP 2026 — 5 at the main conference, 4 at Findings, and 1 at the Demo track!
Our work this cycle spans interpretability, AI safety, computer vision, and multi-agent systems:
📌Main Conference:
• Tracing Multilingual Representations in LLMs with Cross-Layer Transcoders — arxiv.org/abs/2511.10840
• PruneGround: Plug-and-play Spatial Pruning for 3D Visual Grounding — arxiv.org/abs/2606.31148
• How Do Linear Probes Emerge? A Circuit-Tracing Framework with Concept-Targeted Attribution
• Computation Graph Recovery from Chain-of-Thought — openreview.net/pdf?id=OHES5c…
• Preserving Historical Truth: Detecting Historical Revisionism in Large Language Models — arxiv.org/abs/2602.17433
📌Findings:
• Evaluating Second-Order Bias of LLMs Through Epistemic Entitlement — arxiv.org/abs/2606.17506
• How Does Alignment Tuning Shape Representations of Sycophancy and Related Cue-Induced Biases in LLMs? — arxiv.org/pdf/2607.18114
• Simulating Democratic Deliberation: Representing Pluralistic Preferences through Electoral Systems in Multi-Agent LLMs
• Fluid Representations in Reasoning Models — arxiv.org/abs/2602.04843
📌Demo Track:
• CLT-Forge: A Scalable Library for Cross-Layer Transcoders and Attribution Graphs
Huge congratulations to all the authors and collaborators across the lab and our partner institutions.
@AHarrasse1906, @flodraye, @psyonp, @ZhijingJin, @bschoelkopf, Duc Cao Dinh, @leduckhai, Chris Ngo, @TerryJCZhang, @vedantpalit1008, @RoderickWu4, Aydin Javadov, @francescortu, @JoeunYk05, @albecazzaniga, @radamihalcea, @RamaravindM, Raiyan Ahmed, Shion Guha, Syed Ishtiaque Ahmed, Prakhar Gupta, @_rfaulk, @SSzufa, Daniel Hoyer, Roland Bouffanais, @jzl86, Dmitrii Kharlapenko, @iarthsingh, @alesstolfo, @ArthurConmy, @mrinmayasachan, @TonyWu1105, @Jiarui_Liu_, Chih-Hao Hsu.
#EMNLP2026 #NLP #Interpretability #AISafety #MachineLearning
Vision Transformers retweeted
(I'm at #EMNLP2026) Both UofT CS @UofTCompSci and my own @JinesisLab are ALWAYS hiring, every year. Our only bar is talent.
- UofT hires all ranks of Profs in all CS areas, esp. AI. Our call will be released soon.
- My own lab constantly looks for good PhD and pre-doctoral students, anywhere on earth (we have students from 33 different countries). We also welcome any form of research collab, part-time and full-time, remote and in person.
- For PhD students, UofT does not have any committee admission, so any single professor can decide on you directly.
🙋Check out the application forms on my personal website zhijing-jin.com ❤️
Vision Transformers retweeted
I reverse engineered Pocket and made it 10X better.
Right now: the app is full of ads, the microphone is low quality, it constantly misspells peoples names, and has no agentic features.
I changed that.
Vision Transformers retweeted
I've decided to start working on technical alignment research, specifically related to RL and how to fix it (or get rid of it if we must). I will be joining the brilliant folks @resolution_org in Berkeley, a very exciting new alignment bet by @geoffreyirving and others from @AISecurityInst and @TimaeusResearch, as part of the scalable oversight team. I intend to write more about why I'm switching to work on alignment, and why I think more capabilities researchers should really do this. But long story short:
1. We are not on track to solve alignment of superintelligence
2. We will likely have a version of RSI soon, accompanied soon by superintelligence
3. The capabilities recipe works for verifiable, unhackable signal, which the frontier is increasingly diverging from in the pursuit of growing capabilities. It is unclear what RSI means unless we find a scalable recipe to train with proxy rewards.
They might get another prize for it
We’re releasing a broad range of new mathematical results produced by an internal frontier model.
We’ve been consulting with the independent Advisory Group on Mathematics and Artificial Intelligence at the Institute for Advanced Study, and we have drawn on their advice and public recommendations to inform how we release these results.
github.com/openai/math
How is doing pork or panner next?
Can “chicken” make a base model reason? 🐔
Yes!
Why? With the right first tokens, base models can match RL in reasoning. We trace this to learned training data associations and use a simple data edit to make “chicken” elicit reasoning too.
blog: sophielwang.com/cues
Omg it is real!!
Meet Mistral Large 4, aka Le Chonk.
• 1T parameters, natively multimodal. 49B active.
It is the best open weights model from US or Europe on aggregated benchmarks.
• State-of-the-art on critical workloads, including cyber defense, manufacturing and finance and it surpasses closed frontier models on visual grounding.
• Forged in Europe end-to-end and is deployable from Europe via our own Mistral Cloud infrastructure.
• Available to all via API today. Working with cybersecurity partners privately.
Open weights release end of October.
































