Postdoc @MSFTResearch | PhD @uwaterloo | RA @UKPLab l working on search and agents | (https://xitter.cf/t.co/bxTCuxkJWZ, https://xitter.cf/t.co/KYPd6PHDYd, @TREC_RAG and FreshStack)
Bengaluru, India
Joined July 2016
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Life Update: Last month I started as a Postdoctoral Researcher at Microsoft Research India!
If you are curious on why I chose to return to India after my PhD from Canada; I've written some personal thoughts about it!
I'm unfortunately missing #EMNLP2026! But our recent work we observe that Benchmarks such as FreshStack remain "fresh" overtime!
Do give the camera ready version a read: aclanthology.org/2026.findin…
Someone baking some "fresh" wholembed bread here....👀👀
Keep updated here: fresh-stack.github.io
Nandan Thakur retweeted
@beirmug consistently bringing the "Fresh" into FreshStack 🔥
Nandan Thakur retweeted
I think I spy something new on there
Some important trends:
1️⃣ Nemotron-8B/Octen-8B are great open-weight robust dense models to use for retrieval usecases.
2️⃣ Cohere Embed v5 pro is a great closed-source embedding model.
3️⃣ Perplexity late-interaction models (some internal, some open) are great choices to pick.
Nandan Thakur retweeted
I released bekko-static-embedding-v1 🚀, a multilingual static embedding model with state-of-the-art retrieval among static embeddings.
No Transformer layers, no GPU: just token lookup + mean pooling. 40 languages, fast on CPU.
huggingface.co/blog/hotchpot…
Nandan Thakur retweeted
I recently gave an invited lecture on deep research agents for CMU’s AI Agents course, covering evaluation beyond simple factoid QA, RL for open-ended tasks, and a new retrieval paradigm for deep research agents!
youtu.be/nKUBrXFQBUM
Nandan Thakur retweeted
tl;dr - the team @Sahel_Sharify @Lingwei_Gu @nour_jedidi speedran nanochat by @karpathy using ClimbMix by @shizhediao et al. and then SFT'ed on relevance data from @beirmug @crystina_z to arrive at a competitive pointwise reranker: arxiv.org/abs/2610.11922
Petty dismissive to say "some PhD student" who made these benchmark.
Academia tries to incentivise fair, unbiased and open benchmarks focusing on advancing research on a setting, eg zero-shot retrieval with BEIR.
What about the benchmaxxing that companies do to inflate scores?
META’S HEAD OF AI SLAMS AI BENCHMARKS
"it’s some PhD student who made these benchmarks"
"you’re kind of outsourcing all of your product thinking to this viral benchmark"
"so clearly that’s wrong"
I think there is something to this. the whole industry developed so rapidly that there was hardly any time to establish proper benchmarking. companies used what was available, competitors then had to catch up on the same benchmarks, and some of them eventually became industry standards
apparently there is quite a lot of randomness and chance in how we are measuring AI progress today
Nandan Thakur retweeted
Introducing Kylon: The first company harness for your humans, agents and tools.
Whether you’re using Codex, Claude, Devin, Hermes, Gemini CLI, OpenClaw, Muse, Instinct, Grok Bot, Dots, Perplexity Computer, Cursor, Antigravity, Microsoft Autopilot, Amp, Droid, Kiro, Cline, OpenCode, Replit Agent, Lovable, v0, Gmail, Slack, Apollo, Supabase, Apify, Treg, Neon, Vercel, Databricks, Novita, RadixArk, GitHub, Seedance, Fish Audio, Plaud, OpenRouter, Gemini, Google Sheets, TikTok Shop, Outlook, Google Drive, Instagram, Airtable, Google Calendar, Asana, HubSpot, Corgi, SharePoint, Google Tasks, Notion, Google Workspace, Telegram, LINE, Zoho Mail, Meta Ads, Google Search Console, Fathom, ClickUp, Reddit, Granola, Google Docs, OneDrive, tl;dv, Amplitude, Dropbox, YouTube, Ahrefs, Xero, HighLevel, Google Analytics, Google Slides, Monday, QuickBooks, ElevenLabs, Linear, PostHog, LinkedIn, Runway, Zoho Bigin, Stripe, Exa, Cal_com, Box, Google Meet, TikTok, Wise, Mercury, Deel, Replit, Resend, Zoom, Fireflies, Typeform, Canva, Facebook, Google Admin, Hostinger, Google Maps, Retell AI, UptimeRobot, Google Photos, Excel, Cloudflare, Beehiiv, Vapi, 1Password, RunPod, Unipile, Read AI, Figma, Microsoft Teams, Trello, Salesforce, Jira, Confluence, Miro, Coda, Todoist, Basecamp, Wrike, Zendesk, Intercom, Freshdesk, ServiceNow, Gorgias, Mailchimp, ActiveCampaign, MailerLite, SendGrid, Postmark, Customer_io, Gong, Clay, Greenhouse, FreshBooks, Zoho Books, Brex, Ramp, PandaDoc, Google Ads, Microsoft Ads, Pinterest Ads, Semrush, Moz, Mixpanel, Google Tag Manager, Microsoft Clarity, Tableau, Metabase, Snowflake, Google BigQuery, ClickHouse, Wix, Contentful, Sanity, Bubble, Make, GitLab, Bitbucket, Sentry, PagerDuty, Grafana, New Relic, Docker Hub, DigitalOcean, Render, Firebase, Prisma, Algolia, Cloudinary, LaunchDarkly, Postman, Midjourney, Veo, GPT-Image2, NanoBanana, Flux, Kling, MiniMax, Replicate, HuggingFace, DeepSeek, Mistral AI, Ollama, Firecrawl, Eventbrite, Webex, Toggl, Harvest, Tailscale, Mural, Discord, TickTick, Calendly, Attio, Higgsfield, Square, Wispr Flow, Readwise, Bright Data, Google Places, FastMoss, PageSpeed Insights, Kalodata, Cerebras, SerpApi, DataForSEO…
Kylon enables them to work better, together.
Nandan Thakur retweeted
What are we up to? 🏡 Project Greenhouse: Progress Toward Fully Open and Sovereign Agentic Search w/ @Sahel_Sharify @Lingwei_Gu @nour_jedidi arxiv.org/abs/2610.11922
Nandan Thakur retweeted
New company, new team, same frontier results & same open license
Happy to announce the release of our new embedding model family
More than just an upgrade in performance, the models are now multimodal, multi-vectors, and use a shared embedding space to allow cross-model querying
We're releasing pplx-embed-v2-late, two late-interaction embedding models that retrieve text, images, and pages with a shared embedding space for cross-model querying.
Both models achieve frontier performance and are publicly available on Hugging Face.
perplexity.ai/hub/blog/multi…
Nandan Thakur retweeted
We're releasing pplx-embed-v2-late, two late-interaction embedding models that retrieve text, images, and pages with a shared embedding space for cross-model querying.
Both models achieve frontier performance and are publicly available on Hugging Face.
perplexity.ai/hub/blog/multi…
Nandan Thakur retweeted
another release from us, we probably built the strongest late interaction retriever you can find ever, anywhere on earth.
we trained a massive 18B colbert model for text retrieval, image retrieval and document screenshot retrieval, then distilled it to a 9B colbert, and a 0.6B colbert.
Since they from the same teacher, so naturally have shared token embedding space. So you can use 9B offline indexing, 0.6B online search, or use 9B on strong chips and 0.6B locally. Remind 0.6b includes a vision tower as well!
pplx-embed-v2-context, late both out, Dense will be soon. These will marks our new gen or retriever suite.
We're releasing pplx-embed-v2-late, two late-interaction embedding models that retrieve text, images, and pages with a shared embedding space for cross-model querying.
Both models achieve frontier performance and are publicly available on Hugging Face.
perplexity.ai/hub/blog/multi…
Nandan Thakur retweeted
Learning to Retrieve via Reinforcement Learning in Embedding Space
@qiliu6777 et al. train embedding models with reinforcement learning by sampling embeddings and rewarding retrieval, with projection to cut noise.
📝 arxiv.org/abs/2610.07731
👨🏽💻 github.com/liuqi6777/RELER
Nandan Thakur retweeted
Meet EmbeddingGemma 2, our first natively multimodal open model for on-device embeddings.
It expands beyond text to unify code, images, audio, and video in a shared space. 🧵
Nandan Thakur retweeted
Embedding model evaluations has many issues, so that sadly benchmarks like BEIR & MTEB became low signal.
With RCP-nDCG we are here to fix some of these:
cohere.com/blog/rcp-ndcg
arxiv.org/abs/2609.35739
