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From Activation to Intervention: Interpreting Sparse Visual Features through Reconstruction
arXiv:2611.05522v1 Announce Type: new Abstract: Sparse autoencoders (SAEs) decompose visual representations into individual features, offering a promising basis for explaining model behavior. Interpreting these features typically relies on images or pixel regions that most strongly activate them. However, this evidence becomes ambiguous when multiple features with different semantics frequently activate in the same locations, such as texture and color in the same patterns. We propose Reconstructive SAE Intervention (RecSAEI), a framework that shifts feature explanation from passive observation to active visual reconstruction. By intervening on representations along SAE decoder directions and reconstructing the visual output, we provide direct evidence of a feature's semantic meaning. We demonstrate that our method successfully disambiguates feature meanings across multiple datasets and vision encoders. Furthermore, our framework grounds [CLS] SAE interventions in a sparse set of patch SAE decoder directions. Beyond stable target response, this attribution also exposes the compositional relationship between elementary visual patterns and high-level semantics. Our results advocate for intervention reconstruction as an essential complementary signal for explaining neural networks.
The Agentic Analytics Benchmark: ranking tables and labeling failures with Jev
Jev ranks warehouse tables and labels agent failures in under a second, but adding retrieval hints leaves accuracy flat across 20 models on the Agentic Analytics Benchmark.
The new cost-based join optimizer with reordering support for non-inner joins
ClickHouse’s new exhaustive cost-based join optimizer safely reorders inner, outer, semi, and anti joins to find the cheapest valid plan.
How to put generative AI directly into a dbt pipeline with plain SQL
Google Cloud on wiring BigQuery's AI SQL functions into dbt pipelines, from sentiment scoring to LLM-generated product briefings.
Showing our work: a controlled experiment in context engineering
We ran one AI skill 80 times against four context sources. Engineered summaries cut cost 48.5% and run time 43.2%.
From mainframe to AI-powered maturity: Verisk's data modernization journey
Verisk modernized decades of insurance data, from mainframe to dbt, to get AI-ready.
What a swarm of rogue AI agents can teach us about having better ideas
Benn Stancil uses the Hugging Face hack to explain why the most useful ideas come from the weirdest connections.
Trace and evaluate Cursor agents with Langfuse, powered by ClickHouse
Connect Cursor to Langfuse to trace every agent turn, pinpoint expensive model and tool calls, and use evaluations to confirm a fix actually helps, with ClickHouse powering the analytics underneath.
Executable UDFs are now generally available on ClickHouse Cloud
Executable UDFs are now generally available on ClickHouse Cloud across AWS, GCP, and Azure, with native runtimes, network access, observability, and API and Terraform support.
What is direct I/O, and why does ClickHouse Managed Postgres use it for backups?
ClickHouse Managed Postgres uses direct I/O and stripe-sized reads to keep backups fast while protecting the page cache and query latency.
ClickHouse Workload for Microsoft Fabric: sub-second analytics on OneLake, now in public preview
ClickHouse Workload for Microsoft Fabric brings sub-second analytics to OneLake data through a native Fabric workload, with dedicated ClickHouse Cloud compute for agents, dashboards, and applications.
Can your Postgres survive a bad query?
How do Postgres providers handle a query that exhausts memory? A recursive query benchmark compares query failures and cluster survival across ClickHouse Managed Postgres, Cloud SQL, PlanetScale, and Amazon RDS.
chDB Durable Layer for agent memory
The chDB Durable Layer keeps agent memory fast to query locally while making its analytical state recoverable across laptops, CI jobs, and short-lived sandboxes.
ClickHouse release 26.9
ClickHouse 26.9 introduces conditional LIMIT boundaries, incremental refreshes for append-only materialized views, disk spilling for DISTINCT, time-limited tokens, and faster min/max/count queries.