\documentclass[aspectratio=169,11pt]{beamer}
\usepackage[T1]{fontenc}
\usepackage[utf8]{inputenc}
\usepackage{amsmath,amssymb,graphicx,booktabs}

\definecolor{AcademicBlue}{RGB}{26,57,86}
\definecolor{AcademicTeal}{RGB}{0,105,112}
\setbeamercolor{structure}{fg=AcademicBlue}
\setbeamercolor{frametitle}{fg=AcademicBlue,bg=white}
\setbeamercolor{title}{fg=AcademicBlue}
\setbeamercolor{alerted text}{fg=AcademicTeal}
\setbeamercolor{block title}{fg=white,bg=AcademicBlue}
\setbeamercolor{block body}{fg=black,bg=AcademicBlue!5}
\setbeamertemplate{navigation symbols}{}
\setbeamertemplate{itemize items}[circle]
\setbeamertemplate{footline}{%
  \hfill\usebeamerfont{page number in head/foot}%
  \insertframenumber\,/\,\inserttotalframenumber\hspace{4mm}\vspace{3mm}}
\setbeamersize{text margin left=8mm,text margin right=8mm}
\setbeamerfont{frametitle}{size=\Large,series=\bfseries}
\setbeamerfont{title}{size=\LARGE,series=\bfseries}
\newcommand{\papersource}[1]{\par\smallskip{\scriptsize\color{gray}#1\par}}
\newcommand{\K}{\,\mathrm{K}}

\title[Short working contexts]{You don't need long context}
\subtitle{What compaction and selective reading actually establish}
\author{Peter Belcak et al. (12 authors)\\Research-group presentation}
\date{\href{https://arxiv.org/abs/2611.05306v1}{arXiv:2611.05306v1} \quad 5 November 2026}

\begin{document}

\begin{frame}
  \titlepage
  \vspace{-3mm}
  \centering\small Technical report, 3 November 2026
\end{frame}

\begin{frame}{Long work does not always require a long live context}
  \begin{block}{Paper's central test}
    Can an agent finish long-horizon or long-input tasks while each model call sees only compact state and selected evidence?
  \end{block}
  \begin{itemize}
    \item Six long-horizon and seven long-input benchmark suites; primary models: Claude Opus 4.5 and GPT 5.6 Sol.
    \item Usually similar task scores with shorter live contexts; one-action handoffs work on two software tasks, with model-specific losses.
    \item Publication: \href{https://arxiv.org/abs/2611.05306v1}{technical report}; no formal venue found as of 5 Nov 2026.
    \item Paper-specific public code not found as of 5 Nov 2026; the \href{https://github.com/swe-agent/mini-swe-agent}{base mini-swe-agent} is public.
  \end{itemize}
  \papersource{Paper, Abstract and Secs. 2--5, PDF pp. 1, 4--15.}
\end{frame}

\begin{frame}{Longer windows make decoding more expensive}
  \centering
  \includegraphics[width=0.97\linewidth,height=0.57\textheight,keepaspectratio]{figures/fig-01.pdf}
  \begin{itemize}
    \item At 256K versus 16K, cost per output token rises by $3.6\times$ (GLM), $5.6\times$ (Kimi), and $7\times$ (Qwen).
    \item Fixed GPU allocation, cached prefixes, 1{,}024 generated tokens per request; this measures decode throughput.
  \end{itemize}
  \papersource{Paper, Fig. 1 and Sec. 2.5, PDF pp. 1, 8. Original figure from author TeX.}
\end{frame}

\begin{frame}{The paper separates three research questions}
  \begin{enumerate}
    \setlength{\itemsep}{6mm}
    \item \textbf{Long horizon:} Must all past actions and observations remain in the next prompt?
    \item \textbf{Long input:} Must a large source be loaded all at once?
    \item \textbf{One action:} Can a fresh call continue after every individual tool action?
  \end{enumerate}
  \vfill
  \begin{block}{Unifying idea}
    Preserve an action-ready state; keep original evidence available for selective retrieval.
  \end{block}
  \papersource{Paper, Sec. 1, PDF pp. 2--3.}
\end{frame}

\begin{frame}{The test bed spans workflows and document tasks}
  \begin{columns}[T,onlytextwidth]
    \begin{column}{0.49\textwidth}
      \begin{block}{Six long-horizon suites}
        SWE-bench Verified and Multilingual\\
        Terminal-Bench 2.1\\
        FRAMES\\
        $\tau^2$-bench\\
        BFCLv3
      \end{block}
      \small Interaction history grows as the agent acts.
    \end{column}
    \begin{column}{0.49\textwidth}
      \begin{block}{Seven long-input suites}
        LongBench v2, LOONG, AA-LCR\\
        NIAH, RULER\\
        Counting-Stars, BABILong
      \end{block}
      \small Source material is large; the task question may be short.
    \end{column}
  \end{columns}
  \vfill
  \papersource{Paper, Secs. 2.2--2.3, PDF pp. 4--6. Metrics differ by suite.}
\end{frame}

\begin{frame}{Three regimes vary how much history survives}
  \begin{columns}[T,onlytextwidth]
    \begin{column}{0.32\textwidth}
      \begin{block}{FHA: full history}
        Task + every action + every observation stay in one growing conversation.
      \end{block}
      \centering\small Reference for long-horizon tasks
    \end{column}
    \begin{column}{0.32\textwidth}
      \begin{block}{EoCC: threshold}
        At a threshold, write a bounded handoff; restart with original task and compact state.
      \end{block}
      \centering\small Example: 16K limit / 12K threshold
    \end{column}
    \begin{column}{0.32\textwidth}
      \begin{block}{SAC: every action}
        One tool action per conversation; update semantic state before the next fresh call.
      \end{block}
      \centering\small Tested on two coding suites
    \end{column}
  \end{columns}
  \vfill
  \papersource{Paper, Sec. 2.4 and Fig. 3, PDF p. 7. Abbreviations: FHA, EoCC, SAC.}
\end{frame}

\begin{frame}{A useful handoff is an execution brief}
  \begin{block}{What the compact state carries}
    Original task; verified progress; current work and intent; completion criteria; next action; pointers to relevant artifacts.
  \end{block}
  \begin{itemize}
    \item It prioritizes facts needed for future work over a balanced narrative of the past.
    \item The environment and source documents persist across resets.
    \item A lost fact can be recovered if its original record remains searchable.
  \end{itemize}
  \vfill
  \small The paper's coin-toss example shows why a fixed-size handoff alone cannot retain an arbitrarily long sequence of exact observations.
  \papersource{Paper, Sec. 3.1, PDF p. 10; Apps. A and G, PDF pp. 20, 30--31.}
\end{frame}

\begin{frame}{Large inputs become searchable external sources}
  \begin{center}
    \fbox{\parbox{0.86\linewidth}{\centering
      Question + file manifest
      $\;\longrightarrow\;$ search entities and claims
      $\;\longrightarrow\;$ bounded passage reads
      $\;\longrightarrow\;$ answer}}
  \end{center}
  \begin{itemize}
    \item Long inputs are stored as files; Bash searches scan them, while only returned excerpts enter the live conversation.
    \item The provided bounded reader defaults to 6{,}000 characters and caps a read at 8{,}000.
    \item This changes the \emph{input presentation} as well as context policy. On many long-input runs, median compactions are zero.
  \end{itemize}
  \papersource{Paper, Sec. 2.4, PDF p. 7; App. E, PDF pp. 25--26; App. F, PDF pp. 27--29.}
\end{frame}

\begin{frame}{Measure peak context, total work, and task score separately}
  \begin{align*}
    L_{\mathrm{peak}} &= \max_j L_j,\\
    L_{\mathrm{total}} &= \sum_j L_j ,
  \end{align*}
  where $L_j$ is the final length of conversation stretch $j$.
  \begin{itemize}
    \item Peak is the largest live conversation during one task.
    \item Total sums final lengths across restarted conversations; it may exceed the window limit.
    \item Token spend separately counts uncached input, cached input, and generated output.
  \end{itemize}
  \papersource{Paper, Sec. 2.4, PDF p. 7; App. C table captions, PDF pp. 22--23. Equations restate the paper's definitions.}
\end{frame}

\begin{frame}{RQ1: tight limits mostly preserve long-horizon scores}
  \small
  \begin{center}
  \begin{tabular}{lccc}
    \toprule
    Suite & Limit / trigger & Opus baseline $\to$ EoCC & GPT baseline $\to$ EoCC\\
    \midrule
    SWE-bench Verified & 16K / 12K & 70.0 $\to$ 68.8 & 84.4 $\to$ 84.6\\
    SWE-bench Multilingual & 16K / 12K & 66.3 $\to$ 64.3 & 79.3 $\to$ 77.7\\
    Terminal-Bench 2.1 & 16K / 12K & 47.2 $\to$ 48.3 & 87.6 $\to$ 85.4\\
    FRAMES & 16K / 12K & 81.0 $\to$ 79.6 & 90.2 $\to$ 87.1\\
    BFCLv3 & 12K / 8K & 62.0 $\to$ 62.5 & 39.5 $\to$ 39.5\\
    $\tau^2$-bench & 8K / 6K & 79.3 $\to$ 78.8 & 73.9 $\to$ 73.9\\
    \bottomrule
  \end{tabular}
  \end{center}
  \normalsize
  \vspace{2mm}
  Across 22 settings per model, at most five lose more than 2 percentage points.
  \papersource{Paper, Fig. 5, PDF p. 9; App. C, Tables 3--8, PDF pp. 22--23. Scores are suite-specific percentages.}
\end{frame}

\begin{frame}{RQ1: accuracy and priced cost are distinct claims}
  \begin{itemize}
    \setlength{\itemsep}{4mm}
    \item EoCC accuracy changes range from $-3.7$ to $+5.6$ points for Opus and $-3.3$ to $+4.4$ for GPT across six suites and settings.
    \item Modeled optimized-retail savings range from $2.2$--$8.8\times$ and $2.4$--$5.0\times$, respectively.
    \item But on SWE-bench Verified, GPT at 16K / 12K has \emph{higher nominal retail cost}: \$203.12 versus \$167.68 for full history.
  \end{itemize}
  \begin{block}{Interpretation}
    Shorter windows preserve many task scores. The savings multiples assume context-sensitive future prices.
  \end{block}
  \papersource{Paper, Sec. 3 and Fig. 5, PDF pp. 9--10; App. C, Table 3, PDF p. 22.}
\end{frame}

\begin{frame}{RQ2: real document tasks benefit from selective reading}
  \small
  \begin{center}
  \begin{tabular}{llcc}
    \toprule
    Suite & Reported metric & Opus & GPT\\
    \midrule
    LongBench v2 & accuracy, 16K / 12K & 68.0 $\to$ 67.6 & 70.8 $\to$ 72.6\\
    LOONG & mean score, 32K / 24K & 70.1 $\to$ 80.2 & 79.3 $\to$ 84.0\\
    AA-LCR & accuracy, 16K / 12K & 65.0 $\to$ 84.0 & 82.0 $\to$ 83.0\\
    \bottomrule
  \end{tabular}
  \end{center}
  \normalsize
  \begin{itemize}
    \item LOONG perfect-answer rate also rises: Opus 38.9\% $\to$ 53.7\%; GPT 44.4\% $\to$ 52.1\%.
    \item Files remain accessible; only selected passages enter the active context.
  \end{itemize}
  \papersource{Paper, App. F, Tables 11--13, PDF pp. 27--28. Each arrow compares with the full-input baseline.}
\end{frame}

\begin{frame}{RQ2: retrieval-heavy suites show larger gains}
  \small
  \begin{center}
  \begin{tabular}{llcc}
    \toprule
    Suite & Metric & Opus baseline $\to$ selective & GPT baseline $\to$ selective\\
    \midrule
    NIAH & accuracy & 60.0 $\to$ 100.0 & 80.0 $\to$ 100.0\\
    RULER & mean recall & 62.5 $\to$ 95.7 & 65.5 $\to$ 96.4\\
    Counting-Stars & exact match & 65.1 $\to$ 81.2 & 76.6 $\to$ 81.2\\
    BABILong & accuracy & 67.1 $\to$ 92.0 & 70.7 $\to$ 94.0\\
    \bottomrule
  \end{tabular}
  \end{center}
  \normalsize
  NIAH's lexical overlap makes its inserted facts especially searchable. The other suites include aggregation or reasoning beyond locating one sentence.
  \par\smallskip
  \small Modeled optimized-retail savings across seven suites: $6.5$--$54.6\times$ (Opus), $4.4$--$233.3\times$ (GPT); these are not observed bills.
  \papersource{Paper, Sec. 4, Fig. 6, PDF pp. 11--13; App. F, Tables 14--17, PDF pp. 28--29. Selective settings: NIAH 48K/36K; others 16K/12K.}
\end{frame}

\begin{frame}{Document size does not dictate conversation size}
  \begin{columns}[T,onlytextwidth]
    \begin{column}{0.62\textwidth}
      \includegraphics[width=\linewidth,height=0.74\textheight,keepaspectratio]{figures/fig-08.pdf}
    \end{column}
    \begin{column}{0.36\textwidth}
      \begin{itemize}
        \item LOONG: 1{,}600 multi-document questions.
        \item Horizontal: source length, log scale.
        \item Vertical: total conversation length across stretches.
        \item Many runs fall below $y=x$; some remain long.
      \end{itemize}
    \end{column}
  \end{columns}
  \papersource{Paper, Fig. 8, PDF p. 13. Opus at 48K / 40K. Original figure from author TeX.}
\end{frame}

\begin{frame}{RQ3: one action per context remains viable on coding}
  \small
  \begin{center}
  \begin{tabular}{llcccc}
    \toprule
    Model & Suite & FHA score & SAC score & FHA peak & SAC peak\\
    \midrule
    Opus & SWE-bench & 70.0 & 70.2 & 32 / 200 & 8 / 16\\
    Opus & Terminal-Bench & 47.2 & 51.8 & 27 / 130 & 9 / 19\\
    GPT & SWE-bench & 84.4 & 82.4 & 18 / 173 & 7 / 15\\
    GPT & Terminal-Bench & 87.6 & 84.3 & 17 / 133 & 8 / 37\\
    \bottomrule
  \end{tabular}
  \end{center}
  \normalsize
  \begin{itemize}
    \item Scores are percentages; peak context is median / maximum in K tokens per task.
    \item GPT loses 2.0 and 3.3 percentage points; Opus matches or improves.
  \end{itemize}
  \papersource{Paper, Sec. 5, Table 2, PDF p. 14. SAC was tested on these two software suites.}
\end{frame}

\begin{frame}{SSSM is a proposal motivated by SAC, not a trained model}
  \begin{center}
    \fbox{\parbox{0.85\linewidth}{\centering
      Natural-language state $s_t$
      $\;\to\;$ choose action $a_t$
      $\;\to\;$ observe $o_t$
      $\;\to\;$ write updated state $s_{t+1}$}}
  \end{center}
  \begin{itemize}
    \item A Semantic State Space Model would be post-trained to make each state sufficient for a later independent inference call.
    \item Unlike a neural SSM's hidden state, this state is interpretable language.
    \item The paper evaluates SAC with existing models; it does not train or benchmark an SSSM.
  \end{itemize}
  \papersource{Paper, Sec. 5.1, PDF pp. 14--15. Diagram is a teaching restatement of the proposal.}
\end{frame}

\begin{frame}{Measured throughput supplies one cost ratio}
  \[
    r_M=\frac{C_{\mathrm{out},M}(16\K)}
               {C_{\mathrm{out},M}(256\K)}
        =\frac{T_M(256\K)}{T_M(16\K)}.
  \]
  \begin{center}
  \begin{tabular}{lcc}
    \toprule
    Measured model & $r_M$ & 256K / 16K output-cost ratio\\
    \midrule
    GLM 5.2 & 0.2772 & $3.6\times$\\
    Kimi K2.6 & 0.1777 & $5.6\times$\\
    Qwen 3 Coder Next & 0.1439 & $7.0\times$\\
    \bottomrule
  \end{tabular}
  \end{center}
  The paper rounds their mean to $r=0.20$ for its later price model.
  \papersource{Paper, App. B, Eq. (1), PDF p. 20; Fig. 4, PDF p. 8. $T_M$ is sustained decode throughput.}
\end{frame}

\begin{frame}{Reported task-cost savings use a pricing model}
  \[
    \mathrm{EORC}_M(W)=I\,C_{\mathrm{input},M}(W)
      +H\,C_{\mathrm{cached},M}(W)
      +O\,C_{\mathrm{out},M}(W).
  \]
  \begin{itemize}
    \item $I,H,O$: aggregate millions of uncached input, cached input, and output tokens.
    \item Output price is interpolated between 20\% of standard price at 16K and standard price at the reference window.
    \item Input/output and cached/uncached ratios are held fixed.
  \end{itemize}
  \begin{block}{Practical reading}
    These are estimated optimized-retail costs, not observed benchmark invoices or end-to-end latency measurements.
  \end{block}
  \papersource{Paper, App. B, Eqs. (2)--(4), PDF pp. 20--21.}
\end{frame}

\begin{frame}{The boundary is model and workflow dependent}
  \begin{block}{Open-weight stress test}
    Qwen 3 Coder Next drops from 53.0\% to 3.6\% on SWE-bench Verified at 16K / 12K; all five tested open-weight models lose some SWE-bench accuracy at this tightest setting.
  \end{block}
  \begin{itemize}
    \item The paper attributes Qwen's collapse largely to verbose trajectories leaving too little useful work between compactions.
    \item External source access is part of the successful short-context design.
    \item The evidence does not exclude tasks that benefit from a longer live context.
  \end{itemize}
  \papersource{Paper, App. D, Tables 9--10, PDF p. 24; Secs. 3.1 and 6, PDF pp. 10, 15.}
\end{frame}

\begin{frame}{Takeaways for a research group}
  \begin{enumerate}
    \setlength{\itemsep}{5mm}
    \item Distinguish \emph{task duration} and \emph{source size} from the information needed in the next model call.
    \item A compact, action-ready state plus searchable records performs well on many tested workloads.
    \item Evaluate accuracy, peak context, total token spend, actual price, and end-to-end latency separately.
    \item SAC supports the SSSM idea, but the model itself remains a future research direction.
  \end{enumerate}
  \papersource{Paper, Secs. 3--6 and Apps. B, D, PDF pp. 9--15, 20--21, 24.}
\end{frame}

\begin{frame}{Paper and source links}
  \small
  \textbf{Paper:} Peter Belcak, Saurav Muralidharan, Jindong Jiang, Yonggan Fu, Wonmin Byeon, Hanrong Ye, Ryan Angilly, Ali Taghibakhshi, Shih-Yang Liu, Siru Ouyang, Greg Heinrich, and Pavlo Molchanov. \emph{You don't need long context}. Technical report, 2026.\\[4mm]
  \href{https://arxiv.org/abs/2611.05306v1}{Versioned arXiv record} \quad
  \href{https://arxiv.org/pdf/2611.05306v1}{PDF} \quad
  \href{https://arxiv.org/src/2611.05306v1}{Author TeX source}\\[6mm]
  \textbf{Related base harness:} \href{https://github.com/swe-agent/mini-swe-agent}{SWE-agent/mini-swe-agent}. This is not the paper's released experiment code.\\[4mm]
  \textbf{Status checked 5 November 2026:} no formal publication record or paper-specific public code found.
\end{frame}

\end{document}