Hell of volatility in SOFR fixing....Long Live SERFF

SERFF Updates; Tightening camp: Watching for incremental liquidity drain (settlements TGA bills) to firm the front end. Momentum camp: Still comfortable fading any tightening, expecting excess liquidity regime to persist

EFbasis structurally comprehensible to most anyone. the fundamentals make sense. SERFF's fundamentals (not execution, but its "why") quite the can of worms for those who need to understand things before putting money to work behind them. most HR can comprehend EF/b. Not SERFF.

📣 Attn STIR traders and SERFF enjoyers! Finer tick granularity is coming to SOFR-Fed Funds (SERFF) spreads. Beginning next Monday, June 15, SR1-ZQ spreads will trade in 0.25 increments across all contract months.

Regarding the #SERFF situation, something I posted earlier in a GC I’m in w/the usual suspect #STIR bois. None of us RV spread these but how sick are you if you sold it betting on a quick re-lock? 3 full bps of paint dry torture just to scratch…or puke. Eeesh.🤮 $SR1 $FF

SERFF Khalifa innit

let him publish a article about plumbing, and see what serff does tomo.

what happened to your SERFF longs

attn SERFF bois: I wouldn’t be surprised if Warsh took a look at RMPs, said “Wow, you guys over-did it again!” and pushed the FOMC to end them. especially since we’re past April and TGA has returned to 800bn

SOFR-Fed Funds spreads (SERFF) New highs in front three spreads; record volume in M6; parity achieved in N6

SERFF IS SO DEAD right????

recent $root serff filing for NJ as they appear to be nearing a launch

Need Warsh to reduce Balance Sheet, so my SERFF can be bearish xD

Hey @elonmusk How about you add SOFR spreads tickers and curve features $SERFF IS A MUST

SERFF Jun-Jul is 1.5, pretty crazy price even with the EFFR mood swings

Awl mera njerb indrive sraha fih serff

Abril Plesi Daisy Monchi Athena Ringo Nova SimónQ Serff Tetsu Sayaka Henry

SERFF curve 🟢

We took a close look at several public table extraction benchmarks, including OmniDocBench, SCORE-Bench, ParseBench, and RD-TableBench, and found that every one of them has a structural/methodology issue that doesn’t properly evaluate document intelligence at scale. 1/ OmniDocBench's TEDS metric conflates formatting with structure. A 3x3 table with identical content scores differently based on whether it uses <thead> wrappers or plain <tr>. 2/ SCORE-Bench's spatial tolerance parameter can hide serious failures. Drop a financial table's header row, shift data up by one, and the benchmark reports high content accuracy while the headers are gone. 3/ ParseBench relies on frontier VLMs for ground truth, introducing model bias into the benchmark itself. Its table metric treats records as unordered bags, so column transposition and row reordering go unpenalized. The table set is also 503 pages, 54.5% from a single SERFF source, and English-only. 4/ RD-TableBench linearizes 2D tables into 1D sequences, so column swaps can align well despite being structurally wrong. We also audited all 1,000 ground truth files and found 43 with verifiable errors and 89 byte-identical to one provider's output. That's why we built PulseBench-Tab, a benchmark that is multilingual by design, uses 2D-aware scoring that preserves horizontal and vertical adjacency, cleanly separates structure from formatting, and is fully open from the dataset to the scoring code. Full breakdown by the @Pulse__AI team in comments.